REVISION OF TOPOGRAPHIC DATABASES BY SATELLITE IMAGES
|
|
- Chrystal Hensley
- 6 years ago
- Views:
Transcription
1 REVISION OF TOPOGRAPHIC DATABASES BY SATELLITE IMAGES Bettina Petzold Landesvermessungsamt Nordrhein-Westfalen Muffendorfer Str , Bonn Tel.: 0228 / , FAX: petzold@lverma.nrw.de Volker Walter Institut für Photogrammetrie, Universität Stuttgart Geschwister-Scholl-Str. 24, Stuttgart Tel.: 0711 / , FAX: volker.walter@ifp.uni-stuttgart.de KEYWORDS: Updating GIS, High Resolution Imaging Satellites, Laser Data ABSTRACT: This paper examines data from different sensors regarding their potential for an automatic revision of topographic databases. The data which have to be updated are from the German national topographic cartographic database (ATKIS) and were captured in the scale of 1 : 25,000. After a brief introduction into ATKIS the used approach is discussed. Results are shown on examples of data from several sensors: scanned analogue aerial photos, an airborne digital line scanner (DPA camera system), the Indian satellite IRS-1C, the MOMS-2P camera and from a laser scanning system as an additional information source. 1.1 The Basics 1. ATKIS DATA ATKIS, the Authoritative Topographic-Cartographic Information System (Amtliches Topographisch-Kartographisches Informationssystem) is one of the common projects of the Federal Republic of Germany State Survey Working Committee (Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (AdV)). This committee was established to achieve a harmonized development within surveying and mapping which are in Germany within the responsibility of the Federal States. The ATKIS data model is object structured, uses attributes on several levels, allows hierarchical relations between objects and enables the aggregation of objects to complex objects. The data are stored in vector format. 1.2 The Object Class Catalogue and The Digital Landscape Model In the Object Class Catalogue (Objektartenkatalog (OK)) all landscape objects are specified by their definition, their type (point, line or area) and the definition of the accompanying attributes and their possible values. ATKIS objects can be visible real objects of the landscape but also administrative objects as districts and boundaries. The objects are grouped in object classes, subsequently in object groups and finally in object domains. The digital landscape model (Digitales Landschaftsmodell (DLM)) DLM 25 covers the scale domain of about 1: and comes up to the most urgent requirements of most part of the users. That is why it has the highest priority, it is called the basis DLM and is the first to be built up by the Surveying and Mapping Agencies (Landesvermessungsämter (LVermA)) of the Federal States of Germany. A first version of the DLM 25, the so-called DLM 25/1, consists of the most important object classes such as the networks of roads, railways and waters and all covered areas between these network lines. 1.3 Data Capture In Northrhine-Westphalia (NRW) the German Base Map 1:5000 (Deutsche Grundkarte 1:5000, DGK 5)) and the Orthophoto Map 1:5000 (Luftbildkarte, DGK 5L) are used as the main source of information. They guarantee the required precision of ± 3 m and contain the information for capturing of most of the defined attributes. In addition some special small scale maps are necessary, containing attribute information like the type of main roads, of railroads and the river network or some information of power providing companies. A complete coverage of the landscape is guaranteed by a defined group of object classes.
2 2.1 Definitions 2. UPDATING Updating is defined as a systematic revision of the ATKIS database. The updating can be subdivided into the tasks of detecting of field changes, procuring of necessary information and incorporating of new information into the existing database. The procuring of the necessary information is defined as the obtaining of information extensive enough for updating. That means the geometry has to have the required precision, and the semantic information has to be complete. The distinction between detecting and procuring is necessary because only some of the possible methods of data acquisition are supporting both parts. Regarding satellite data it directly depends on the ground resolution whether those data can be used to detect changes only or in addition to procure the information necessary for updating. Finally the incorporation is the introduction of the acquired information into the ATKIS-database. 2.2 Information Sources old and new The field check and the topographic information service have the oldest tradition as they have already been used for the updating of analogue topographic maps. But the working methods for the updating of ATKIS have to be changed because there are much more information in ATKIS than in analogue maps and the data have to be updated continuously and not only periodically. Low or medium spatial resolution satellite images are already used for the updating of geographic information systems in scales of 1: or smaller, for example SPOT or LANDSAT data [Konecny 96]. The ground pixel size of 10 m or more prevents those data from fulfilling the requirements for the updating of ATKIS. More interesting could be the data of the Modular Optoelectronic Multispectral Stereo Scanner (MOMS) which is installed on the PRIRODA module of the Russian MIR station. MOMS has a high resolution panchromatic sensor with a ground pixel size of about 7 m. First MOMS data have been scheduled for the beginning of 1996 and were the first possibility to test a remote sensing product with a good spatial resolution, even though not sufficient for the updating of ATKIS. New high resolution satellite systems are presently under development and will be operational soon - thus high resolution remote sensing data will also be available soon [Jacobson 98], [Kilston 98] which will have a resolution which should be sufficient for the update of ATKIS. 3.1 The General Idea 3. PROJECT In spring 1994 the German Space Agency (DARA), today the German Aerospace Centre (DLR), called for proposals for special exemplary projects using MOMS data for administrational tasks. A financial support should be given for those projects which should help to contribute to the exploration of new fields of application of remote sensing. The LVermA NRW took the opportunity, and finally the project was supported by the DLR. The objectives of the project are formulated as follows: - an automatic approach for the detection of temporal changes in ATKIS based on satellite orthophotos has to be developed; - as a result of the project the information was expected which object classes can be updated by the developed approach and under which conditions. The objective is not only to update the geometry but, if possible, also the attributes. The strategy for the updating should fit especially into MOMS data, but also to images with a higher spatial resolution up to already existing scanned analogue orthophotos. That is done first because satellites with a high spatial resolution are already announced [Fritz 97]. And second it should be sure that the algorithms would even fit into orthophotos for the worst case that no up-to-date satellite images could be used, for example after a long bad weather period. The developed approach should help at least in the most tiring and hence the most error-prone part of updating, the task of detecting the field changes by manual revision of orthophotos. 3.2 The Approach The approach is fully automatic and can be subdivided in general into two steps (see figure 1). First the data are classified with a supervised Maximum Likelihood Classification, then the classification result has to be compared with existing ATKIS objects. In the following more general details of the process are given whereas some test results are discussed in chapter 4.
3 sensor data (multispectral + preprocessed channels) training areas derived from GIS database supervised maximum likelihood classification GIS data pixel oriented classification result matching of classified data and GIS database revised GIS data Figure 1: Overview of the automatic verification of ATKIS The Training Areas and Classification The training areas are not digitized manually by an operator but they are derived automatically from the existing ATKIS data. This is possible if the number of field changes or possible restitution errors is considerably lower than all ATKIS objects. For more details of the generation of training areas see [Walter 98]. Not all ATKIS object classes can be distinguished only by spectral or textural specifications. Therefore the ATKIS object classes are grouped together into six land use classes. These are forest, agricultural areas, garden, settlement, water and street. In addition the class shadow can be defined. For the land use class settlement data with low resolution (e. g. > 15 m) can be used as they are. But data with higher resolution necessitate special preparations: the training areas should consist only of those pixels that represent buildings. To achieve this, a buffer around streets is computed and pixels which represent vegetation are deleted with the help of the vegetation index. The remaining pixels are most probably buildings. It only makes sense to define a land use class street if the ground pixel size is of 3 to 4 m or less. Otherwise the pixels are more or less mixed pixels leading to no result. For the training areas only those pixel laying on the middle axis of the object are used, and vegetation pixels are again deleted. In addition, all streets laying in forests are not used for the training areas Matching After the classification it must be decided which of the GIS objects do not match the classification result. This can be objects where a change in the landscape has occurred or objects, that were not collected correctly. All GIS objects are subdivided into three classes. The first class contains all objects which could be detected with a high certainty, the second class contains all objects which are detected only partly and the third class contains all objects which could not be detected at all. The decision to which class an object belongs is made by measuring the percentage of pixels which are classified to the same object class as the object in the GIS database belongs to. Besides the percentage of correctly classified pixels, also the homogeneity of the classification result and the form are used for verification. A detailed description of the
4 DPA & ATKIS classification matching a) greenland b) settlement streets settlement greenland forest full verified partly verified not found Figure 2: Examples for classification and matching of DPA data verification of the classification results can be found in [Walter 1988], [Walter, Fritsch 98]. Figure 2 shows some results of the verification of ATKIS objects in DPA images. The DPA images are represented in the original resolution in the left column. The ATKIS geometry is superimposed in black. The classification result in a resolution of 2 m is represented in the middle column and the result of the verification in the right column. Figure 2 a) shows two objects which were captured in ATKIS as greenland. In the DPA image it can be seen that meanwhile a settlement area was built up. The result of the verification is that these two objects cannot be found in the image because of the low number of pixels that were classified as greenland. Figure 2 b) shows an settlement objects which contains a big greenland area. The object is marked as partly found because at least the left part of the object was classified as settlement or streets. 4. TESTS WITH DIFFERENT DATA SETS When starting the project no MOMS scene of Northrhine-Westphalia was available. That is why other data were tested first. 4.1 IRS Data As more and more problems with the MIR station occurred, the DLR decided to provide all participants of MOMS projects with IRS images. The IRS-1C consists of a high resolution panchromatic sensor (PAN) with a spatial resolution of 5.8 m and a radiometric resolution of 6 bit, a sensor with four multispectral channels with a radiometric resolution
5 IRS-1c (cir) DPA classification ATKIS water forest settlement green land Figure 3: Classification of IRS-1C data of 7 bit and a spatial resolution of 23.5 m and finally a wide field sensor (WiFS) with a spatial resolution of 188 m which is without any interest for our purposes. More details about the Indian Remote Sensing Satellite (IRS) 1C can be found in [Srivastava 96]. The expectations concerning those data were not too high because of the low spatial resolution but the data enabled a test of the algorithms and a first confirmation that the basic idea is correct. Figure 3 shows a classification result of IRS-1C data. The image is shown as a CIR image because IRS-1C does not capture data in the blue range. In order to improve the interpretation of the IRS-1C image, a DPA image of the same area is represented. The results confirmed that the developed algorithms are working correctly. But the further processing of the data is very problematic. If an object is marked as not found it would be very difficult for an operator to decide if there has been a change or not if he has only IRS-1C data as a decision base. 4.2 MOMS Data The German MOMS sensor (Modular Optoelectronic Multispectral Stereo Scanner) is installed on the PRIRODA module which is docked to the Russian MIR station. The scanner consists of a high resolution panchromatic 3-line stereo module and a multispectral camera with a red, green, blue and near infrared channel. The ground resolution is 7 m for the panchromatic nadir channel, 15 m for the forward and backward looking channel and 18 m for the multispectral data. For more information on the MOMS camera see [Schiewe 98]. A pixel precise classification is partially possible here. But problems appear for example between the differentiation of the land use classes settlement and forest. The explanation for these problems was found quickly: the radiometry of that MOMS scene was very bad, in all channels less than 100 grey values are used. One detail of the classification result can be seen in Figure DPA Data DPA is a digital line array scanner system with four multispectral channels (red, green, blue, and near infrared) and a three-line panchromatic stereo module, very similar to the MOMS camera but developed for airborne applications. The radiometric resolution is 8 bit for all channels. The ground pixel size directly depends of the flight height above ground. A flight height of 1900 m results for example in a ground pixel size of 60 cm for the multispectral channels and 30 cm for the stereo images.
6 MOMS-2P (cir) classification orthophoto (rgb) ATKIS other water forest settlement green land Figure 4: Classification of MOMS-2P data The data of the test area have a ground pixel size of 0.75 m. This corresponds at an area of 2 km * 2 km to a pixel number of more than seven million pixels. Because the classification is a complex process, this leads to a high computing time as well as high memory requirements. However, experiences show that depending on the land use class high resolution must not necessarily improve the results of the classification. In order to find a compromise between quality of change detection and computer requirements the data were resampled to a pixel size of 2 m. Examples of the classification of DPA data are shown in Figure Usage of Laser Data Because the software was developed in such a way that there exist no restrictions regarding the input data for the classification it is also possible to use laser data as an additional information channel. First we tested the combination of standard rgborthophotos and laser data. But it had to be stated that even with the known height information derived from laser data the result was very week in some parts of the image. Especially in shadow regions the classification was bad. That can be stated especially for the land use class street. The spectral characteristics of street pixels laying in the sun are very different from those laying in the shadow. Another test was the classification of cir orthophotos with and without laser data. The laser data improve the classification result significantly because they have a complementary behaviour as multispectral data. With laser data the classes agricultural areas and street can be separated very good from the classes forest and settlement because of the different heights of the pixels above the ground whereas in multispectral data the classes farmland and forest can be separated very good from the classes streets and settlement because of the strongly different percentage of chlorophyll. One classification result is shown in Figure 5. Shadow areas can be classified considerably better in cir images than in rgb images. In areas with very strong shadow it comes also to wrong classifications in cir images, however considerably less than in rgb images. Our next strategy was to introduce an additional land use class shadow in the classification process. Because there is no information in the GIS database about shadow areas, they have to be derived from the laser data. For the automatic generation of training areas for shadow the local height which is provided by the laser data as well as the elevation and azimuth of the sun at the time of image acquisition is required. The elevation and azimuth of the sun can either be determined manually by an interactive measurement of the edge of a shadowed area in the image and the
7 orthophoto (cir) classification without laser data classification with laser data forest greenland settlement streets Figure 5: Classification without and with laser data corresponding object height in the normalized laser data or derived automatically from the geographical latitude and longitude of the captured area and the time of image acquisition. Figure 6 shows the automatic processed training areas for the additional land use class shadow. In order to avoid the shadow class in the final result the approach can be further refined by splitting each of the land use classes into one land use class for shadow areas and one land use class for non shadow areas. After the classification the shadow and non shadow pixels for each land use class are combined again to obtain one unique class for each type of land use. The final result of the classification algorithm for the whole test area is given in Figure FINAL RESULTS AND CONCLUSIONS In this paper the potential of data from different sensors for the verification of ATKIS is examined. Data from the sensors IRS-1C and MOMS-2P are only to a limited extent suitable for this approach because of their low resolution. Objects must have a size of 2 * 2 pixel so that they can be recognized certainly in the figure. A recognition of line objects, like streets, is not possible. A big problem is the verification of the classification results. Even if objects can be recognized pixel precisely, it is not possible for an operator to verify the results without further information sources. With data from the DPA camera system, good results can be achieved. Area objects can be recognized in a sufficient accuracy for ATKIS. Figure 6: Automatic generation of training areas for shadow
8 orthophoto classification water forest greenland settlement streets Figure 7: Classification using 10 classes (5 for shadow areas and 5 for non shadow areas) However, in inner city areas street pixels may be classified as houses and vice versa. This is a problem for the road detection. A reliable detection of roads with DPA data is possible only in sparsely populated areas. The DPA data were resampled to a resolution of 2 m. This falls into the range which will be covered by future high resolution satellite systems (see for example [Jacobsen 98] or [Kilston 98]). With the availability of such systems an automated verification of data in the scale 1:25,000 based on up-to-date data will be possible. The tests showed that it is not possible to achieve homogenous good classification results based on rgb data. Even the addition of laser data does not lead to sufficient results. A channel in the nir range is necessary to handle shadowed areas. The best results were achieved with the combination of cir images and laser data. With this combination it is also possible to verify objects in larger scales down to 1:2.500 (for example ground plans of buildings) if the images and the laser data are captured in a high resolution. This is also the topic which we want to focus our future work on. A problem is the definition of quality measures to compare the performance of the different sensors. The classification result can be described with statistical measures such as the Kappa value or other measures described in the literature [Congalton 91] [Rosenfeld and Fritzpatrick-Lins 86] [Stehmann 97] but it is difficult to define a quality measure for the object verification. Problems especially appear with objects that are captured according to ownership structures and not to detectable structures in the image. Objects of this kind often have inhomogeneous spectral and textural characteristics and even a human operator is not able to decide if there is a change in the landscape or not if he has no additional information source. Also different operator will come to different decisions. Another problem is that some object classes are defined ambiguously and therefore are not clearly delimitable from each other. In order to get a more practical oriented idea of the quality of the results we will install the developed software package at the Surveying Institute of the State of Northrhine- Westphalia. Extensive data sets will be processed there and the results will be evaluated by ATKIS professionals. 6. REFERENCES Congalton, R. 1991: A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sensing Environment 37, Fritz, L. W.: August 1997 Status of New Commercial Earth Observation Satellite Systems; Photogrammetrie, Fernerkundung, Geoinformation 6/1997, p Hahn, M., Stallmann, D., Staetter, C. 1996: The DPA-Sensor System for Topographic and Thematic
9 Mapping; in: International Archives of Photogrammetry and Remote Sensing, Vol XXXI, Part B2, p Jacobsen, K. 1998: Status and Tendency of Sensors for Mapping, Proceedings of the International Symposium on Earth Observation System for sustainable Development, Bangladore, India, Vol. XXXII, Part I of International Archives of Photogrammetry and Remote Sensing (ISPRS), Remote Sensing (ISPRS), Vol XXXII, Part 3/1, p Walter, V. 1999; Comparison of the potential of different sensors for an automatic approach for change detection in GIS databases; in: International Workshop on Integrated Spatial Databases: Images and GIS, accepted for publishing. Kilston, S. 1998: Capabilities of new Remote Sensing Satellites to support sustainable Development, Proceedings of the International Symposium on Earth Observation System for sustainable Development, Bangladore, India, Vol. XXXII, Part I of International Archives of Photogrammetry and Remote Sensing (ISPRS), Konecny, G. 1996: International Mapping from Space; International Archives of Photogrammetry and Remote Sensing, Vol. XXXI, Part B4, Vienna Petzold, B. 1998; High Resolution Satellite Images For The Updating Of Geotopographic Basis Information; in: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover Nr. 227, Festschrift Univ.-Prof. Dr.-Ing. Dr.h.c. mult. Gottfried Konecny zur Emeritierung, p ; Schiewe, J.: MOMS-02: Gelungenes Experiment ohne Zukunft?; Photogrammetrie, Fernerkundung, Geoinformation 1/1998, p Srivastava, P. 1996; Cartographic Potential of IRS- 1C Data Products; in: International Archives of Photogrammetry and Remote Sensing, Vol. 31, Part B4, page Stehmann, V. 1997: Selecting and Interpreting Measures of Thematic Classification Accuracy, Remote Sensing Environment 62, Walter, V. 1998; Automatic classification of remote sensing data for GIS database revision; in: ISPRS Commission IV Symposium on GIS Between Visions and Applications, Stuttgart, Germany, p Walter, V., Fritsch, D. 1998; Automatic verification of GIS data using high resolution multispectral data; in; International Archives of Photogrammetry and
Automated GIS data collection and update
Walter 267 Automated GIS data collection and update VOLKER WALTER, S tuttgart ABSTRACT This paper examines data from different sensors regarding their potential for an automatic change detection approach.
More informationHigh Resolution Sensor Test Comparison with SPOT, KFA1000, KVR1000, IRS-1C and DPA in Lower Saxony
High Resolution Sensor Test Comparison with SPOT, KFA1000, KVR1000, IRS-1C and DPA in Lower Saxony K. Jacobsen, G. Konecny, H. Wegmann Abstract The Institute for Photogrammetry and Engineering Surveys
More informationEXAMPLES OF TOPOGRAPHIC MAPS PRODUCED FROM SPACE AND ACHIEVED ACCURACY CARAVAN Workshop on Mapping from Space, Phnom Penh, June 2000
EXAMPLES OF TOPOGRAPHIC MAPS PRODUCED FROM SPACE AND ACHIEVED ACCURACY CARAVAN Workshop on Mapping from Space, Phnom Penh, June 2000 Jacobsen, Karsten University of Hannover Email: karsten@ipi.uni-hannover.de
More informationCOMPARISON OF INFORMATION CONTENTS OF HIGH RESOLUTION SPACE IMAGES
COMPARISON OF INFORMATION CONTENTS OF HIGH RESOLUTION SPACE IMAGES H. Topan*, G. Büyüksalih*, K. Jacobsen ** * Karaelmas University Zonguldak, Turkey ** University of Hannover, Germany htopan@karaelmas.edu.tr,
More informationDIFFERENTIAL APPROACH FOR MAP REVISION FROM NEW MULTI-RESOLUTION SATELLITE IMAGERY AND EXISTING TOPOGRAPHIC DATA
DIFFERENTIAL APPROACH FOR MAP REVISION FROM NEW MULTI-RESOLUTION SATELLITE IMAGERY AND EXISTING TOPOGRAPHIC DATA Costas ARMENAKIS Centre for Topographic Information - Geomatics Canada 615 Booth Str., Ottawa,
More informationGENERATION AND UPDATE OF VMAP DATA USING SATELLITE AND AIRBORNE IMAGERY
GENERATION AND UPDATE OF VMAP DATA USING SATELLITE AND AIRBORNE IMAGERY T. Ohlhof 1, T. Emge 1, W. Reinhardt 2, K. Leukert 2, C. Heipke 3, K. Pakzad 3 1 Elektroniksystem- und Logistik-GmbH, PO Box 800569,
More informationTEMPORAL ANALYSIS OF MULTI EPOCH LANDSAT GEOCOVER IMAGES IN ZONGULDAK TESTFIELD
TEMPORAL ANALYSIS OF MULTI EPOCH LANDSAT GEOCOVER IMAGES IN ZONGULDAK TESTFIELD Şahin, H. a*, Oruç, M. a, Büyüksalih, G. a a Zonguldak Karaelmas University, Zonguldak, Turkey - (sahin@karaelmas.edu.tr,
More informationAbstract Quickbird Vs Aerial photos in identifying man-made objects
Abstract Quickbird Vs Aerial s in identifying man-made objects Abdullah Mah abdullah.mah@aramco.com Remote Sensing Group, emap Division Integrated Solutions Services Department (ISSD) Saudi Aramco, Dhahran
More informationModule 3 Introduction to GIS. Lecture 8 GIS data acquisition
Module 3 Introduction to GIS Lecture 8 GIS data acquisition GIS workflow Data acquisition (geospatial data input) GPS Remote sensing (satellites, UAV s) LiDAR Digitized maps Attribute Data Management Data
More informationNew remote sensing sensors and imaging products for the monitoring of urban dynamics
Geoinformation for European-wide Integration, Benes (ed.) 2003 Millpress, Rotterdam, ISBN 90-77017-71-2 New remote sensing sensors and imaging products for the monitoring of urban dynamics Matthias Möller
More informationTopographic mapping from space K. Jacobsen*, G. Büyüksalih**
Topographic mapping from space K. Jacobsen*, G. Büyüksalih** * Institute of Photogrammetry and Geoinformation, Leibniz University Hannover ** BIMTAS, Altunizade-Istanbul, Turkey KEYWORDS: WorldView-1,
More informationCanImage. (Landsat 7 Orthoimages at the 1: Scale) Standards and Specifications Edition 1.0
CanImage (Landsat 7 Orthoimages at the 1:50 000 Scale) Standards and Specifications Edition 1.0 Centre for Topographic Information Customer Support Group 2144 King Street West, Suite 010 Sherbrooke, QC
More informationHIGH RESOLUTION COLOR IMAGERY FOR ORTHOMAPS AND REMOTE SENSING. Author: Peter Fricker Director Product Management Image Sensors
HIGH RESOLUTION COLOR IMAGERY FOR ORTHOMAPS AND REMOTE SENSING Author: Peter Fricker Director Product Management Image Sensors Co-Author: Tauno Saks Product Manager Airborne Data Acquisition Leica Geosystems
More informationRemote Sensing. The following figure is grey scale display of SPOT Panchromatic without stretching.
Remote Sensing Objectives This unit will briefly explain display of remote sensing image, geometric correction, spatial enhancement, spectral enhancement and classification of remote sensing image. At
More informationAn Introduction to Geomatics. Prepared by: Dr. Maher A. El-Hallaq خاص بطلبة مساق مقدمة في علم. Associate Professor of Surveying IUG
An Introduction to Geomatics خاص بطلبة مساق مقدمة في علم الجيوماتكس Prepared by: Dr. Maher A. El-Hallaq Associate Professor of Surveying IUG 1 Airborne Imagery Dr. Maher A. El-Hallaq Associate Professor
More informationINFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE
INFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE M. Alkan a, * a Department of Geomatics, Faculty of Civil Engineering, Yıldız Technical University,
More informationRADIOMETRIC AND GEOMETRIC CHARACTERISTICS OF PLEIADES IMAGES
RADIOMETRIC AND GEOMETRIC CHARACTERISTICS OF PLEIADES IMAGES K. Jacobsen a, H. Topan b, A.Cam b, M. Özendi b, M. Oruc b a Leibniz University Hannover, Institute of Photogrammetry and Geoinformation, Germany;
More informationGeoBase Raw Imagery Data Product Specifications. Edition
GeoBase Raw Imagery 2005-2010 Data Product Specifications Edition 1.0 2009-10-01 Government of Canada Natural Resources Canada Centre for Topographic Information 2144 King Street West, suite 010 Sherbrooke,
More informationFusion of Heterogeneous Multisensor Data
Fusion of Heterogeneous Multisensor Data Karsten Schulz, Antje Thiele, Ulrich Thoennessen and Erich Cadario Research Institute for Optronics and Pattern Recognition Gutleuthausstrasse 1 D 76275 Ettlingen
More informationNON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS
NON-PHOTOGRAPHIC SYSTEMS: Multispectral Scanners Medium and coarse resolution sensor comparisons: Landsat, SPOT, AVHRR and MODIS CLASSIFICATION OF NONPHOTOGRAPHIC REMOTE SENSORS PASSIVE ACTIVE DIGITAL
More informationREMOTE SENSING INTERPRETATION
REMOTE SENSING INTERPRETATION Jan Clevers Centre for Geo-Information - WU Remote Sensing --> RS Sensor at a distance EARTH OBSERVATION EM energy Earth RS is a tool; one of the sources of information! 1
More informationDigital database creation of historical Remote Sensing Satellite data from Film Archives A case study
Digital database creation of historical Remote Sensing Satellite data from Film Archives A case study N.Ganesh Kumar +, E.Venkateswarlu # Product Quality Control, Data Processing Area, NRSA, Hyderabad.
More informationAerial photography: Principles. Frame capture sensors: Analog film and digital cameras
Aerial photography: Principles Frame capture sensors: Analog film and digital cameras Overview Introduction Frame vs scanning sensors Cameras (film and digital) Photogrammetry Orthophotos Air photos are
More informationTELLS THE NUMBER OF PIXELS THE TRUTH? EFFECTIVE RESOLUTION OF LARGE SIZE DIGITAL FRAME CAMERAS
TELLS THE NUMBER OF PIXELS THE TRUTH? EFFECTIVE RESOLUTION OF LARGE SIZE DIGITAL FRAME CAMERAS Karsten Jacobsen Leibniz University Hannover Nienburger Str. 1 D-30167 Hannover, Germany jacobsen@ipi.uni-hannover.de
More informationLand Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana. Geob 373 Remote Sensing. Dr Andreas Varhola, Kathry De Rego
1 Land Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana Geob 373 Remote Sensing Dr Andreas Varhola, Kathry De Rego Zhu an Lim (14292149) L2B 17 Apr 2016 2 Abstract Montana
More informationAPCAS/10/21 April 2010 ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-THIRD SESSION. Siem Reap, Cambodia, April 2010
APCAS/10/21 April 2010 Agenda Item 8 ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-THIRD SESSION Siem Reap, Cambodia, 26-30 April 2010 The Use of Remote Sensing for Area Estimation by Robert
More informationCHAPTER 7: Multispectral Remote Sensing
CHAPTER 7: Multispectral Remote Sensing REFERENCE: Remote Sensing of the Environment John R. Jensen (2007) Second Edition Pearson Prentice Hall Overview of How Digital Remotely Sensed Data are Transformed
More informationPOTENTIAL OF LARGE FORMAT DIGITAL AERIAL CAMERAS. Dr. Karsten Jacobsen Leibniz University Hannover, Germany
POTENTIAL OF LARGE FORMAT DIGITAL AERIAL CAMERAS Dr. Karsten Jacobsen Leibniz University Hannover, Germany jacobsen@ipi.uni-hannover.de Introduction: Digital aerial cameras are replacing traditional analogue
More informationCartographical Potential of MOMS-02/D2 Image Data
Schiewe 95 Cartographical Potential of MOMS-02/D2 Image Data JOCHEN SCHIEWE, Hannover ABSTRACT Due to a reduced pixel size of 4.5 m and an along-track stereo capability data from the space sensor MOMS-02
More informationEXAMPLES OF OBJECT-ORIENTED CLASSIFICATION PERFORMED ON HIGH-RESOLUTION SATELLITE IMAGES
EXAMPLES OF OBJECT-ORIENTED CLASSIFICATION... 349 Stanisław Lewiński, Karol Zaremski EXAMPLES OF OBJECT-ORIENTED CLASSIFICATION PERFORMED ON HIGH-RESOLUTION SATELLITE IMAGES Abstract: Information about
More informationSommersemester Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur.
Basics of Remote Sensing Some literature references Franklin, SE 2001 Remote Sensing for Sustainable Forest Management Lewis Publishers 407p Lillesand, Kiefer 2000 Remote Sensing and Image Interpretation
More informationSection 2 Image quality, radiometric analysis, preprocessing
Section 2 Image quality, radiometric analysis, preprocessing Emmanuel Baltsavias Radiometric Quality (refers mostly to Ikonos) Preprocessing by Space Imaging (similar by other firms too): Modulation Transfer
More informationSatellite Remote Sensing: Earth System Observations
Satellite Remote Sensing: Earth System Observations Land surface Water Atmosphere Climate Ecosystems 1 EOS (Earth Observing System) Develop an understanding of the total Earth system, and the effects of
More informationUSE OF LANDSAT 7 ETM+ DATA AS BASIC INFORMATION FOR INFRASTRUCTURE PLANNING
USE OF LANDSAT 7 ETM+ DATA AS BASIC INFORMATION FOR INFRASTRUCTURE PLANNING H. Rüdenauer, M. Schmitz University of Duisburg-Essen, Dept. of Civil Engineering, 45117 Essen, Germany ruedenauer@uni-essen.de,
More informationCALIBRATION OF OPTICAL SATELLITE SENSORS
CALIBRATION OF OPTICAL SATELLITE SENSORS KARSTEN JACOBSEN University of Hannover Institute of Photogrammetry and Geoinformation Nienburger Str. 1, D-30167 Hannover, Germany jacobsen@ipi.uni-hannover.de
More informationEO Data Today and Application Fields. Denise Petala
EO Data Today and Application Fields Denise Petala ! IGD GROUP AE "Infotop SA, Geomet Ltd., Dynatools Ltd. "Equipment and know how in many application fields, from surveying till EO data and RS. # Leica,
More informationSatellite data processing and analysis: Examples and practical considerations
Satellite data processing and analysis: Examples and practical considerations Dániel Kristóf Ottó Petrik, Róbert Pataki, András Kolesár International LCLUC Regional Science Meeting in Central Europe Sopron,
More informationAdvanced Techniques in Urban Remote Sensing
Advanced Techniques in Urban Remote Sensing Manfred Ehlers Institute for Geoinformatics and Remote Sensing (IGF) University of Osnabrueck, Germany mehlers@igf.uni-osnabrueck.de Contents Urban Remote Sensing:
More informationCHARACTERISTICS OF VERY HIGH RESOLUTION OPTICAL SATELLITES FOR TOPOGRAPHIC MAPPING
CHARACTERISTICS OF VERY HIGH RESOLUTION OPTICAL SATELLITES FOR TOPOGRAPHIC MAPPING K. Jacobsen Leibniz University Hannover, Institute of Photogrammetry and Geoinformation jacobsen@ipi.uni-hannover.de Commission
More informationPROCEEDINGS - AAG MIDDLE STATES DIVISION - VOL. 21, 1988
PROCEEDINGS - AAG MIDDLE STATES DIVISION - VOL. 21, 1988 SPOTTING ONEONTA: A COMPARISON OF SPOT 1 AND landsat 1 IN DETECTING LAND COVER PATTERNS IN A SMALL URBAN AREA Paul R. Baumann Department of Geography
More informationImportant Missions. weather forecasting and monitoring communication navigation military earth resource observation LANDSAT SEASAT SPOT IRS
Fundamentals of Remote Sensing Pranjit Kr. Sarma, Ph.D. Assistant Professor Department of Geography Mangaldai College Email: prangis@gmail.com Ph. No +91 94357 04398 Remote Sensing Remote sensing is defined
More informationNORMALIZING ASTER DATA USING MODIS PRODUCTS FOR LAND COVER CLASSIFICATION
NORMALIZING ASTER DATA USING MODIS PRODUCTS FOR LAND COVER CLASSIFICATION F. Gao a, b, *, J. G. Masek a a Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA b Earth
More informationDEM GENERATION WITH WORLDVIEW-2 IMAGES
DEM GENERATION WITH WORLDVIEW-2 IMAGES G. Büyüksalih a, I. Baz a, M. Alkan b, K. Jacobsen c a BIMTAS, Istanbul, Turkey - (gbuyuksalih, ibaz-imp)@yahoo.com b Zonguldak Karaelmas University, Zonguldak, Turkey
More informationImage interpretation and analysis
Image interpretation and analysis Grundlagen Fernerkundung, Geo 123.1, FS 2014 Lecture 7a Rogier de Jong Michael Schaepman Why are snow, foam, and clouds white? Why are snow, foam, and clouds white? Today
More informationThe studies began when the Tiros satellites (1960) provided man s first synoptic view of the Earth s weather systems.
Remote sensing of the Earth from orbital altitudes was recognized in the mid-1960 s as a potential technique for obtaining information important for the effective use and conservation of natural resources.
More informationremote sensing? What are the remote sensing principles behind these Definition
Introduction to remote sensing: Content (1/2) Definition: photogrammetry and remote sensing (PRS) Radiation sources: solar radiation (passive optical RS) earth emission (passive microwave or thermal infrared
More informationIntroduction of Satellite Remote Sensing
Introduction of Satellite Remote Sensing Spatial Resolution (Pixel size) Spectral Resolution (Bands) Resolutions of Remote Sensing 1. Spatial (what area and how detailed) 2. Spectral (what colors bands)
More informationRemote Sensing Platforms
Types of Platforms Lighter-than-air Remote Sensing Platforms Free floating balloons Restricted by atmospheric conditions Used to acquire meteorological/atmospheric data Blimps/dirigibles Major role - news
More informationRemote sensing in archaeology from optical to lidar. Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts
Remote sensing in archaeology from optical to lidar Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts Introduction Optical remote sensing Systems Search for
More informationUSGS Welcome. 38 th CEOS Working Group on Calibration and Validation Plenary (WGCV-38)
Landsat 5 USGS Welcome Prepared for 38 th CEOS Working Group on Calibration and Validation Plenary (WGCV-38) Presenter Tom Cecere International Liaison USGS Land Remote Sensing Program Elephant Butte Reservoir
More informationUrban Feature Classification Technique from RGB Data using Sequential Methods
Urban Feature Classification Technique from RGB Data using Sequential Methods Hassan Elhifnawy Civil Engineering Department Military Technical College Cairo, Egypt Abstract- This research produces a fully
More informationUrban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images
Urban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images Fumio YAMAZAKI/ yamazaki@edm.bosai.go.jp Hajime MITOMI/ mitomi@edm.bosai.go.jp Yalkun YUSUF/ yalkun@edm.bosai.go.jp
More information9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011
Training Course Remote Sensing Basic Theory & Image Processing Methods 19 23 September 2011 Popular Remote Sensing Sensors & their Selection Michiel Damen (September 2011) damen@itc.nl 1 Overview Low resolution
More informationSpectral Signatures. Vegetation. 40 Soil. Water WAVELENGTH (microns)
Spectral Signatures % REFLECTANCE VISIBLE NEAR INFRARED Vegetation Soil Water.5. WAVELENGTH (microns). Spectral Reflectance of Urban Materials 5 Parking Lot 5 (5=5%) Reflectance 5 5 5 5 5 Wavelength (nm)
More informationA METHOD FOR ADAPTING GLOBAL IMAGE SEGMENTATION METHODS TO IMAGES OF DIFFERENT RESOLUTIONS
A METHOD FOR ADAPTING GLOBAL IMAGE SEGMENTATION METHODS TO IMAGES OF DIFFERENT RESOLUTIONS P. Hofmann c, Josef Strobl a, Thomas Blaschke a a Z_GIS, Zentrum für Geoinformatik, Paris-Lodron-Universität Salzburg,
More informationANALYSIS OF SRTM HEIGHT MODELS
ANALYSIS OF SRTM HEIGHT MODELS Sefercik, U. *, Jacobsen, K.** * Karaelmas University, Zonguldak, Turkey, ugsefercik@hotmail.com **Institute of Photogrammetry and GeoInformation, University of Hannover,
More informationA map says to you, 'Read me carefully, follow me closely, doubt me not.' It says, 'I am the Earth in the palm of your hand. Without me, you are alone
A map says to you, 'Read me carefully, follow me closely, doubt me not.' It says, 'I am the Earth in the palm of your hand. Without me, you are alone and lost. Beryl Markham (West With the Night, 1946
More informationInt n r t o r d o u d c u ti t on o n to t o Remote Sensing
Introduction to Remote Sensing Definition of Remote Sensing Remote sensing refers to the activities of recording/observing/perceiving(sensing)objects or events at far away (remote) places. In remote sensing,
More informationAn Introduction to Remote Sensing & GIS. Introduction
An Introduction to Remote Sensing & GIS Introduction Remote sensing is the measurement of object properties on Earth s surface using data acquired from aircraft and satellites. It attempts to measure something
More informationMonitoring agricultural plantations with remote sensing imagery
MPRA Munich Personal RePEc Archive Monitoring agricultural plantations with remote sensing imagery Camelia Slave and Anca Rotman University of Agronomic Sciences and Veterinary Medicine - Bucharest Romania,
More informationUltraCam and UltraMap Towards All in One Solution by Photogrammetry
Photogrammetric Week '11 Dieter Fritsch (Ed.) Wichmann/VDE Verlag, Belin & Offenbach, 2011 Wiechert, Gruber 33 UltraCam and UltraMap Towards All in One Solution by Photogrammetry ALEXANDER WIECHERT, MICHAEL
More informationImage interpretation I and II
Image interpretation I and II Looking at satellite image, identifying different objects, according to scale and associated information and to communicate this information to others is what we call as IMAGE
More informationChapter 1 Overview of imaging GIS
Chapter 1 Overview of imaging GIS Imaging GIS, a term used in the medical imaging community (Wang 2012), is adopted here to describe a geographic information system (GIS) that displays, enhances, and facilitates
More informationOutline. Introduction. Introduction: Film Emulsions. Sensor Systems. Types of Remote Sensing. A/Prof Linlin Ge. Photographic systems (cf(
GMAT x600 Remote Sensing / Earth Observation Types of Sensor Systems (1) Outline Image Sensor Systems (i) Line Scanning Sensor Systems (passive) (ii) Array Sensor Systems (passive) (iii) Antenna Radar
More informationFROM THE FIELD SHEET TO THE COMPLETE DIGITAL WORKFLOW
FROM THE FIELD SHEET TO THE COMPLETE DIGITAL WORKFLOW Martin Gurtner Swisstopo, Federal Office of Topography, CH-3084 Wabern, Switzerland, martin.gurtner@swisstopo.ch Abstract The Swiss Federal Office
More informationDISTINGUISHING URBAN BUILT-UP AND BARE SOIL FEATURES FROM LANDSAT 8 OLI IMAGERY USING DIFFERENT DEVELOPED BAND INDICES
DISTINGUISHING URBAN BUILT-UP AND BARE SOIL FEATURES FROM LANDSAT 8 OLI IMAGERY USING DIFFERENT DEVELOPED BAND INDICES Mark Daryl C. Janiola (1), Jigg L. Pelayo (1), John Louis J. Gacad (1) (1) Central
More informationRGB colours: Display onscreen = RGB
RGB colours: http://www.colorspire.com/rgb-color-wheel/ Display onscreen = RGB DIGITAL DATA and DISPLAY Myth: Most satellite images are not photos Photographs are also 'images', but digital images are
More informationUSE OF DIGITAL AERIAL IMAGES TO DETECT DAMAGES DUE TO EARTHQUAKES
USE OF DIGITAL AERIAL IMAGES TO DETECT DAMAGES DUE TO EARTHQUAKES Fumio Yamazaki 1, Daisuke Suzuki 2 and Yoshihisa Maruyama 3 ABSTRACT : 1 Professor, Department of Urban Environment Systems, Chiba University,
More informationDEMS BASED ON SPACE IMAGES VERSUS SRTM HEIGHT MODELS. Karsten Jacobsen. University of Hannover, Germany
DEMS BASED ON SPACE IMAGES VERSUS SRTM HEIGHT MODELS Karsten Jacobsen University of Hannover, Germany jacobsen@ipi.uni-hannover.de Key words: DEM, space images, SRTM InSAR, quality assessment ABSTRACT
More informationREMOTE SENSING FOR FLOOD HAZARD STUDIES.
REMOTE SENSING FOR FLOOD HAZARD STUDIES. OPTICAL SENSORS. 1 DRS. NANETTE C. KINGMA 1 Optical Remote Sensing for flood hazard studies. 2 2 Floods & use of remote sensing. Floods often leaves its imprint
More informationMULTIRESOLUTION SPOT-5 DATA FOR BOREAL FOREST MONITORING
MULTIRESOLUTION SPOT-5 DATA FOR BOREAL FOREST MONITORING M. G. Rosengren, E. Willén Metria Miljöanalys, P.O. Box 24154, SE-104 51 Stockholm, Sweden - (mats.rosengren, erik.willen)@lm.se KEY WORDS: Remote
More informationTechnical Evaluation of Khartoum State Mapping Project
Technical Evaluation of Khartoum State Mapping Project Nagi Zomrawi 1 and Mohammed Fator 2 1 School of Surveying Engineering, Collage of Engineering, Sudan University of Science and Technology, Khartoum,
More informationGIS Data Collection. Remote Sensing
GIS Data Collection Remote Sensing Data Collection Remote sensing Introduction Concepts Spectral signatures Resolutions: spectral, spatial, temporal Digital image processing (classification) Other systems
More informationImproving the Quality of Satellite Image Maps by Various Processing Techniques RUEDIGER TAUCH AND MARTIN KAEHLER
Improving the Quality of Satellite Image Maps by Various Processing Techniques RUEDIGER TAUCH AND MARTIN KAEHLER Technical University of Berlin Photogrammetry and Cartography StraBe des 17.Juni 135 Berlin,
More informationRemote Sensing in Daily Life. What Is Remote Sensing?
Remote Sensing in Daily Life What Is Remote Sensing? First time term Remote Sensing was used by Ms Evelyn L Pruitt, a geographer of US in mid 1950s. Minimal definition (not very useful): remote sensing
More informationIKONOS High Resolution Multispectral Scanner Sensor Characteristics
High Spatial Resolution and Hyperspectral Scanners IKONOS High Resolution Multispectral Scanner Sensor Characteristics Launch Date View Angle Orbit 24 September 1999 Vandenberg Air Force Base, California,
More informationUSING MULTISPECTRAL SATELLITE IMAGES FOR UP-DATING VECTOR DATA IN A GEODATABASE
JOURNAL OF APPLIED ENGINEERING SCIENCES VOL. 1(14), issue 4_2011 ISSN 2247-3769 ISSN-L 2247-3769 (Print) / e-issn:2284-7197 USING MULTISPECTRAL SATELLITE IMAGES FOR VAIS Manuel Bucharest University, e-mail:
More informationPROFILE BASED SUB-PIXEL-CLASSIFICATION OF HEMISPHERICAL IMAGES FOR SOLAR RADIATION ANALYSIS IN FOREST ECOSYSTEMS
PROFILE BASED SUB-PIXEL-CLASSIFICATION OF HEMISPHERICAL IMAGES FOR SOLAR RADIATION ANALYSIS IN FOREST ECOSYSTEMS Ellen Schwalbe a, Hans-Gerd Maas a, Manuela Kenter b, Sven Wagner b a Institute of Photogrammetry
More informationUniversity of Kota Kota
University of Kota Kota Diploma in Remote Sensing and GIS SYLLABUS 2017 1 Diploma in Remote Sensing And GIS (DRSGIS) Exam.-2016-17 Title of the Course: Diploma in Remote Sensing And GIS Duration of the
More informationROLE OF SATELLITE DATA APPLICATION IN CADASTRAL MAP AND DIGITIZATION OF LAND RECORDS DR.T. RAVISANKAR GROUP HEAD (LRUMG) RSAA/NRSC/ISRO /DOS HYDERABAD
ROLE OF SATELLITE DATA APPLICATION IN CADASTRAL MAP AND DIGITIZATION OF LAND RECORDS DR.T. RAVISANKAR GROUP HEAD (LRUMG) RSAA/NRSC/ISRO /DOS HYDERABAD WORKSHOP on Best Practices under National Land Records
More informationProcessing of stereo scanner: from stereo plotter to pixel factory
Photogrammetric Week '03 Dieter Fritsch (Ed.) Wichmann Verlag, Heidelberg, 2003 Bignone 141 Processing of stereo scanner: from stereo plotter to pixel factory FRANK BIGNONE, ISTAR, France ABSTRACT With
More informationGeometry perfect Radiometry unknown?
Institut für Photogrammetrie Geometry perfect Radiometry unknown? Photogrammetric Week 2011 Stuttgart Michael Cramer Institut für Photogrammetrie () Universität Stuttgart michael.cramer@.uni-stuttgart.de
More information9/12/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011
Training Course Remote Sensing Basic Theory & Image Processing Methods 19 23 September 2011 Remote Sensing Platforms Michiel Damen (September 2011) damen@itc.nl 1 Overview Platforms & missions aerial surveys
More informationModule 11 Digital image processing
Introduction Geo-Information Science Practical Manual Module 11 Digital image processing 11. INTRODUCTION 11-1 START THE PROGRAM ERDAS IMAGINE 11-2 PART 1: DISPLAYING AN IMAGE DATA FILE 11-3 Display of
More informationLand cover change methods. Ned Horning
Land cover change methods Ned Horning Version: 1.0 Creation Date: 2004-01-01 Revision Date: 2004-01-01 License: This document is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.
More informationSample Copy. Not For Distribution.
Photogrammetry, GIS & Remote Sensing Quick Reference Book i EDUCREATION PUBLISHING Shubham Vihar, Mangla, Bilaspur, Chhattisgarh - 495001 Website: www.educreation.in Copyright, 2017, S.S. Manugula, V.
More informationGovt. Engineering College Jhalawar Model Question Paper Subject- Remote Sensing & GIS
Govt. Engineering College Jhalawar Model Question Paper Subject- Remote Sensing & GIS Time: Max. Marks: Q1. What is remote Sensing? Explain the basic components of a Remote Sensing system. Q2. What is
More informationHYPERSPECTRAL IMAGERY FOR SAFEGUARDS APPLICATIONS. International Atomic Energy Agency, Vienna, Austria
HYPERSPECTRAL IMAGERY FOR SAFEGUARDS APPLICATIONS G. A. Borstad 1, Leslie N. Brown 1, Q.S. Bob Truong 2, R. Kelley, 3 G. Healey, 3 J.-P. Paquette, 3 K. Staenz 4, and R. Neville 4 1 Borstad Associates Ltd.,
More informationInteractive comment on PRACTISE Photo Rectification And ClassificaTIon SoftwarE (V.2.0) by S. Härer et al.
Geosci. Model Dev. Discuss., 8, C3504 C3515, 2015 www.geosci-model-dev-discuss.net/8/c3504/2015/ Author(s) 2015. This work is distributed under the Creative Commons Attribute 3.0 License. Interactive comment
More informationUniversity of Texas at San Antonio EES 5053 Term Project CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM + IMAGERY NEWFEL MAZARI
University of Texas at San Antonio EES 5053 Term Project CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM + IMAGERY NEWFEL MAZARI Introduction and Objectives The present study is a correlation
More information[GEOMETRIC CORRECTION, ORTHORECTIFICATION AND MOSAICKING]
2013 Ogis-geoInfo Inc. IBEABUCHI NKEMAKOLAM.J [GEOMETRIC CORRECTION, ORTHORECTIFICATION AND MOSAICKING] [Type the abstract of the document here. The abstract is typically a short summary of the contents
More informationFOREST MAPPING IN MONGOLIA USING OPTICAL AND SAR IMAGES
FOREST MAPPING IN MONGOLIA USING OPTICAL AND SAR IMAGES D.Enkhjargal 1, D.Amarsaikhan 1, G.Bolor 1, N.Tsetsegjargal 1 and G.Tsogzol 1 1 Institute of Geography and Geoecology, Mongolian Academy of Sciences
More informationMSB Imagery Program FAQ v1
MSB Imagery Program FAQ v1 (F)requently (A)sked (Q)uestions 9/22/2016 This document is intended to answer commonly asked questions related to the MSB Recurring Aerial Imagery Program. Table of Contents
More informationStatistical Analysis of SPOT HRV/PA Data
Statistical Analysis of SPOT HRV/PA Data Masatoshi MORl and Keinosuke GOTOR t Department of Management Engineering, Kinki University, Iizuka 82, Japan t Department of Civil Engineering, Nagasaki University,
More informationGE 113 REMOTE SENSING
GE 113 REMOTE SENSING Topic 8. Image Classification and Accuracy Assessment Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information
More informationUpdate on Landsat Program and Landsat Data Continuity Mission
Update on Landsat Program and Landsat Data Continuity Mission Dr. Jeffrey Masek LDCM Deputy Project Scientist NASA GSFC, Code 923 November 21, 2002 Draft LDCM Implementation Phase RFP Overview Page 1 Celebrate!
More informationHIGH RESOLUTION IMAGERY FOR MAPPING AND LANDSCAPE MONITORING
HIGH RESOLUTION IMAGERY FOR MAPPING AND LANDSCAPE MONITORING Karsten Jacobsen Leibniz University Hannover, Institute of Photogrammetry and Geoinformation Nienburger Str. 1, 30165 Hannover, Germany, jacobsen@ipi.uni-hannover.de
More informationPOTENTIAL OF MANUAL AND AUTOMATIC FEATURE EXTRACTION FROM HIGH RESOLUTION SPACE IMAGES IN MOUNTAINOUS URBAN AREAS
POTENTIAL OF MANUAL AND AUTOMATIC FEATURE EXTRACTION FROM HIGH RESOLUTION SPACE IMAGES IN MOUNTAINOUS URBAN AREAS H. Topan a, *, M. Oruç a, K. Jacobsen b a ZKU, Engineering Faculty, Dept. of Geodesy and
More informationSources of Geographic Information
Sources of Geographic Information Data properties: Spatial data, i.e. data that are associated with geographic locations Data format: digital (analog data for traditional paper maps) Data Inputs: sampled
More informationBlacksburg, VA July 24 th 30 th, 2010 Remote Sensing Page 1. A condensed overview. For our purposes
A condensed overview George McLeod Prepared by: With support from: NSF DUE-0903270 in partnership with: Geospatial Technician Education Through Virginia s Community Colleges (GTEVCC) The art and science
More information