Basic Digital Image Processing. The Structure of Digital Images. An Overview of Image Processing. Image Restoration: Line Drop-outs
|
|
- Susan Cooper
- 6 years ago
- Views:
Transcription
1 Basic Digital Image Processing A Basic Introduction to Digital Image Processing ~~~~~~~~~~ Rev. Ronald J. Wasowski, C.S.C. Associate Professor of Environmental Science University of Portland Portland, Oregon 24 September 2015 The structure of digital images An image processing overview Image restoration Image enhancement Information extraction Image processing hardware & software The Structure of Digital Images An array of pixels Picture elements Rows & columns of pixels Rows are horizontal Columns are vertical Lines & samples of pixels Lines are horizontal Samples are vertical Pixels contain a numerical value DN Digital number Lowest value is black Highest value is white An Overview of Image Processing Three fundamental categories Image restoration Images often include defects of various kinds Image enhancement Images often need to be made more readable Information extraction This is always the ultimate goal Image Restoration: Line Drop-outs Part or all of some image lines are missing Scanner or recorder malfunction Data transmission drop-outs Reconstruct the missing data Use filters to estimate missing pixel values Linear, bilinear & cubic interpolation algorithms Multiple adjacent image lines are missing Landsat 7 scan line corrector failure Image Restoration: Banding All sensors change over time & at different rates Multiple sensors in every scanner system 6 image lines per EW scan for Landsat MSS data 16 image lines per EW scan for Landsat TM data 2048 image lines per NS path for pushbroom sensors Calculate DN x & σ for each scan line set Force x & σ to be equal for entire scan line set Worst just before sensor recalibration Satellite pushbroom scanners almost impossible Landsat images rotated to North almost impossible
2 Image Restoration: Line Offsets Satellites orbit from N ~11 E to S ~11 W Constant sunlight illumination azimuth Satellite s orbit precesses exactly once per year Earth rotates from W to E under the satellite Image acquisition takes 7 to 25 seconds Image provider offsets scan lines Use appropriate software Every satellite scanner system is different Satellite roll may introduce additional offsets Landsat ETM+ Scan Edge Effects Landsat ETM+ Scan Line Pattern Image Restoration: Random Noise Imaging sensor instabilities Satellite electronic subsystem instabilities Voltage spikes & dips Data transmission instabilities Severe thunderstorms in data transmission path Improved subsystems quality Appropriate filtering of resulting image data Satellites are not designed to be serviceable Severe degradation makes imagery useless Restoration: Atmospheric Scattering Scattering degrades information content Scattering is selective Rayleigh scattering Blue light scattered most & reflected infrared light least Discard blue spectral band Scattergrams estimate amount of scattering Pixels from very dark areas (e.g., water & lava) Calculate least squares regression line Subtract intercept DN value from every pixel No dark areas available to calculate intercept Variable scattering in different image areas Restoration: Geometric Distortions Relief displacement High elevations displaced away from center Low elevations displaced toward center Imaging platform motions Roll Wing tips up or down Pitch Nose tips up or down Yaw Nose turns into the wind Imaging system malfunctions Failure to properly offset scan lines Landsat 7 scan line corrector failure
3 Relief Displacement Geometry Aerial Photo Relief Displacement Imaging Platform Roll, Pitch & Yaw Landsat 7 Scan Line Corrector (SLC) Mount Hood: 25 August 2012 Image Enhancement: Contrast Common Contrast Stretches Entire brightness range seldom used Distinguish details in both lava fields & glacier ice Most images appear quite dark & low in contrast Spread out DN values over brightness range Force some pixels to black & others to white Saturate some number or percent of pixels to 0 & 255 Default is often 1.00% saturation or 0.39% saturation Spread out other DN s using various algorithms Linear, Gaussian, histogram equalization Everyone s visual perception is different Linear DN s are spread evenly between 0 & 255 Decisions are made regarding percent saturation Gaussian DN s nearly a bell curve between 0 & 255 Some flexibility in choosing the value for σ Histogram equalization DN s are spread unevenly between 0 & 255 Cumulative frequency distribution a straight line
4 Image Enhancement: Density Slicing The human eye has limited color perception Human eyes only perceive ~ 1,500 colors Computer screens have great color capability Computer screens display ~ 16 million colors Drastically reduce number of displayed colors Inaccurate color representation Inherent limitations of 3-color displays RGB Sharp Aquos televisions are 4-color displays RGBY Image Enhancement: Edges Linear features on images are often subtle All satellite imagery tends to be low contrast Use filters that increase contrast along edges Directional algorithms Only enhance lines trending in a particular direction Selectively accentuate faults zones, joint sets, ridges Non-directional algorithms Equally enhance lines trending in all directions Non-linear features may remain low contrast Image Enhancement: Sharpening Non-linear images features are often subtle Tendency of satellite imagery to be low contrast Employ filters that increase local contrast High-pass filters Low-pass filters Linear features may remain low contrast Image Enhancement: Digital Mosaics Entire area not covered by one image Obtain enough images to cover entire area Stitch the images together into a mosaic Match geometry at edges of images Match contrast of adjacent images Match color of adjacent images Lighting differences in different seasons Land cover differences in different seasons Image Enhancement: Data Merging Spatial resolution seldom as good as desired Satellites acquire high-resolution pan band Typically twice as good as multispectral bands Landsat ETM+ 30 m multispectral & 15 m pan French SPOT 20 m multispectral & 10 m pan Use of alternative color spaces RGB Human eyes sensitive to red, green & blue IHS Intensity, hue [ color ] & saturation [vividness] Procedure Convert 3 appropriate bands from RGB into IHS Double band size by pixel replication Replace intensity with high-resolution pan band Convert from IHS back into RGB Image Enhancement: Synthetic Stereo Visual interpretation may benefit from stereo Obtain appropriate satellite image Obtain appropriate DEM Generate synthetic left & right stereo images Print & view with traditional stereo viewers View on-screen with special hardware & software DEM s may have poor resolution DEM spacing much larger than image pixel size Vertical accuracy may be especially bad
5 Information Extraction: PCA Principal Components Analysis The problem of spectral autocorrelation Adjacent bands may contain same information Visually apparent in scattergrams DN values of two spectral bands displayed on a graph Procedure Generate new set of synthetic spectral bands Input as many bands as desired Usually all available spectral bands Output as many bands as desired Usually only 3 spectral bands No more than the number of input spectral bands Successive PCA images look less like the original scene Minimize autocorrelation between spectral bands Specify the percent information content in each PCA band Information Extraction: Ratio Images Spectral bands pairs may contain information Both positive & negative correlations Carefully design ratio images Simple ratios Normalized ratios Vegetation index images VI images NDVI Normalized difference vegetation index NDVI = (IR1 Red) / (IR1 + Red) Confusing influence of soil moisture Specialized VI algorithms Information Extraction: Classification Abundant information in multispectral data Supervised multispectral classification The user does know what is in the scene The user designates areas of each land cover/use type Training sites Multispectral color definitions calculated from training sites Unsupervised multispectral classification The user does not know what is in the scene The computer finds colors that are actually there Multispectral color definitions calculated by sampling pixels Assumption that color correlates with land cover Fresh asphalt & deep clear water are indistinguishable Information Extraction: Change Monitor various kinds of environmental change Use multi-date imagery Raw spectral bands Classified or transformed images Calculation of change vectors Similar to statistical trend lines Appropriate imagery in not always available Mount St. Helens Generic Image Processing Software Adobe PhotoShop Import a wide variety of image formats Limited to BSQ (band sequential) format Monochrome, RGB color & CMYK color Wide variety of image enhancements Contrast, color, sharpness, filters etc. Export a wide variety of image formats BMP, GIF, JPG, TIF & many others Irfanview Excellent public domain software Windows only Dedicated Image Processing Software Public domain MicroMSI Attempt to do things better Designed as a teaching tool Works only under Windows Proprietary Erdas Imagine De facto world standard Works under Windows & Unix operating systems Steep learning curve
Image Fusion. Pan Sharpening. Pan Sharpening. Pan Sharpening: ENVI. Multi-spectral and PAN. Magsud Mehdiyev Geoinfomatics Center, AIT
1 Image Fusion Sensor Merging Magsud Mehdiyev Geoinfomatics Center, AIT Image Fusion is a combination of two or more different images to form a new image by using certain algorithms. ( Pohl et al 1998)
More informationRemote sensing image correction
Remote sensing image correction Introductory readings remote sensing http://www.microimages.com/documentation/tutorials/introrse.pdf 1 Preprocessing Digital Image Processing of satellite images can be
More informationApplication of GIS to Fast Track Planning and Monitoring of Development Agenda
Application of GIS to Fast Track Planning and Monitoring of Development Agenda Radiometric, Atmospheric & Geometric Preprocessing of Optical Remote Sensing 13 17 June 2018 Outline 1. Why pre-process remotely
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 informationEnhancement of Multispectral Images and Vegetation Indices
Enhancement of Multispectral Images and Vegetation Indices ERDAS Imagine 2016 Description: We will use ERDAS Imagine with multispectral images to learn how an image can be enhanced for better interpretation.
More informationThe techniques with ERDAS IMAGINE include:
The techniques with ERDAS IMAGINE include: 1. Data correction - radiometric and geometric correction 2. Radiometric enhancement - enhancing images based on the values of individual pixels 3. Spatial enhancement
More informationMod. 2 p. 1. Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur
Histograms of gray values for TM bands 1-7 for the example image - Band 4 and 5 show more differentiation than the others (contrast=the ratio of brightest to darkest areas of a landscape). - Judging from
More informationImages and Graphics. 4. Images and Graphics - Copyright Denis Hamelin - Ryerson University
Images and Graphics Images and Graphics Graphics and images are non-textual information that can be displayed and printed. Graphics (vector graphics) are an assemblage of lines, curves or circles with
More informationGE 113 REMOTE SENSING. Topic 7. Image Enhancement
GE 113 REMOTE SENSING Topic 7. Image Enhancement Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information Technology Caraga State
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 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 informationMODULE 4 LECTURE NOTES 4 DENSITY SLICING, THRESHOLDING, IHS, TIME COMPOSITE AND SYNERGIC IMAGES
MODULE 4 LECTURE NOTES 4 DENSITY SLICING, THRESHOLDING, IHS, TIME COMPOSITE AND SYNERGIC IMAGES 1. Introduction Digital image processing involves manipulation and interpretation of the digital images so
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 informationLecture 13: Remotely Sensed Geospatial Data
Lecture 13: Remotely Sensed Geospatial Data A. The Electromagnetic Spectrum: The electromagnetic spectrum (Figure 1) indicates the different forms of radiation (or simply stated light) emitted by nature.
More informationECC419 IMAGE PROCESSING
ECC419 IMAGE PROCESSING INTRODUCTION Image Processing Image processing is a subclass of signal processing concerned specifically with pictures. Digital Image Processing, process digital images by means
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 informationPreparing Remote Sensing Data for Natural Resources Mapping (image enhancement, rectifications )
Preparing Remote Sensing Data for Natural Resources Mapping (image enhancement, rectifications ) Why is this important What are the major approaches Examples of digital image enhancement Follow up exercises
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 informationRemoving Thick Clouds in Landsat Images
Removing Thick Clouds in Landsat Images S. Brindha, S. Archana, V. Divya, S. Manoshruthy & R. Priya Dept. of Electronics and Communication Engineering, Avinashilingam Institute for Home Science and Higher
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 informationSECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS
RADT 3463 - COMPUTERIZED IMAGING Section I: Chapter 2 RADT 3463 Computerized Imaging 1 SECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS RADT 3463 COMPUTERIZED IMAGING Section I: Chapter 2 RADT
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 informationINTEGRATED DEM AND PAN-SHARPENED SPOT-4 IMAGE IN URBAN STUDIES
INTEGRATED DEM AND PAN-SHARPENED SPOT-4 IMAGE IN URBAN STUDIES G. Doxani, A. Stamou Dept. Cadastre, Photogrammetry and Cartography, Aristotle University of Thessaloniki, GREECE gdoxani@hotmail.com, katerinoudi@hotmail.com
More informationImage Processing for feature extraction
Image Processing for feature extraction 1 Outline Rationale for image pre-processing Gray-scale transformations Geometric transformations Local preprocessing Reading: Sonka et al 5.1, 5.2, 5.3 2 Image
More informationImage Band Transformations
Image Band Transformations Content Band math Band ratios Vegetation Index Tasseled Cap Transform Principal Component Analysis (PCA) Decorrelation Stretch Image Band Transformation Purposes Image band transforms
More informationGEOG432: Remote sensing Lab 3 Unsupervised classification
GEOG432: Remote sensing Lab 3 Unsupervised classification Goal: This lab involves identifying land cover types by using agorithms to identify pixels with similar Digital Numbers (DN) and spectral signatures
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 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 information8. EDITING AND VIEWING COORDINATES, CREATING SCATTERGRAMS AND PRINCIPAL COMPONENTS ANALYSIS
Editing and viewing coordinates, scattergrams and PCA 8. EDITING AND VIEWING COORDINATES, CREATING SCATTERGRAMS AND PRINCIPAL COMPONENTS ANALYSIS Aim: To introduce you to (i) how you can apply a geographical
More informationDigital Image Processing
Digital Image Processing 1 Patrick Olomoshola, 2 Taiwo Samuel Afolayan 1,2 Surveying & Geoinformatic Department, Faculty of Environmental Sciences, Rufus Giwa Polytechnic, Owo. Nigeria Abstract: This paper
More informationMidterm Review. Image Processing CSE 166 Lecture 10
Midterm Review Image Processing CSE 166 Lecture 10 Topics covered Image acquisition, geometric transformations, and image interpolation Intensity transformations Spatial filtering Fourier transform and
More informationRemote Sensing. Odyssey 7 Jun 2012 Benjamin Post
Remote Sensing Odyssey 7 Jun 2012 Benjamin Post Definitions Applications Physics Image Processing Classifiers Ancillary Data Data Sources Related Concepts Outline Big Picture Definitions Remote Sensing
More informationDIGITALGLOBE ATMOSPHERIC COMPENSATION
See a better world. DIGITALGLOBE BEFORE ACOMP PROCESSING AFTER ACOMP PROCESSING Summary KOBE, JAPAN High-quality imagery gives you answers and confidence when you face critical problems. Guided by our
More informationRADIOMETRIC CALIBRATION
1 RADIOMETRIC CALIBRATION Lecture 10 Digital Image Data 2 Digital data are matrices of digital numbers (DNs) There is one layer (or matrix) for each satellite band Each DN corresponds to one pixel 3 Digital
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 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 informationGEOMETRIC RECTIFICATION OF EUROPEAN HISTORICAL ARCHIVES OF LANDSAT 1-3 MSS IMAGERY
GEOMETRIC RECTIFICATION OF EUROPEAN HISTORICAL ARCHIVES OF LANDSAT -3 MSS IMAGERY Torbjörn Westin Satellus AB P.O.Box 427, SE-74 Solna, Sweden tw@ssc.se KEYWORDS: Landsat, MSS, rectification, orbital model
More informationAtmospheric interactions; Aerial Photography; Imaging systems; Intro to Spectroscopy Week #3: September 12, 2018
GEOL 1460/2461 Ramsey Introduction/Advanced Remote Sensing Fall, 2018 Atmospheric interactions; Aerial Photography; Imaging systems; Intro to Spectroscopy Week #3: September 12, 2018 I. Quick Review from
More informationRemote Sensing for Rangeland Applications
Remote Sensing for Rangeland Applications Jay Angerer Ecological Training June 16, 2012 Remote Sensing The term "remote sensing," first used in the United States in the 1950s by Ms. Evelyn Pruitt of the
More informationCourse overview; Remote sensing introduction; Basics of image processing & Color theory
GEOL 1460 /2461 Ramsey Introduction to Remote Sensing Fall, 2018 Course overview; Remote sensing introduction; Basics of image processing & Color theory Week #1: 29 August 2018 I. Syllabus Review we will
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 informationGEOI 313: Digital Image Processing - I
GEOI 33: Digital Image Processing - I Module I: Image Representation Image Display For remote sensing computing, the image display is especially important because the analyst must be able to examine images
More informationIntroduction to Remote Sensing Part 1
Introduction to Remote Sensing Part 1 A Primer on Electromagnetic Radiation Digital, Multi-Spectral Imagery The 4 Resolutions Displaying Images Corrections and Enhancements Passive vs. Active Sensors Radar
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 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 informationChapter 5. Preprocessing in remote sensing
Chapter 5. Preprocessing in remote sensing 5.1 Introduction Remote sensing images from spaceborne sensors with resolutions from 1 km to < 1 m become more and more available at reasonable costs. For some
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 informationIntroduction to Remote Sensing. Electromagnetic Energy. Data From Wave Phenomena. Electromagnetic Radiation (EMR) Electromagnetic Energy
A Basic Introduction to Remote Sensing (RS) ~~~~~~~~~~ Rev. Ronald J. Wasowski, C.S.C. Associate Professor of Environmental Science University of Portland Portland, Oregon 1 September 2015 Introduction
More informationBackground. Computer Vision & Digital Image Processing. Improved Bartlane transmitted image. Example Bartlane transmitted image
Background Computer Vision & Digital Image Processing Introduction to Digital Image Processing Interest comes from two primary backgrounds Improvement of pictorial information for human perception How
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 informationPresent and future of marine production in Boka Kotorska
Present and future of marine production in Boka Kotorska First results from satellite remote sensing for the breeding areas of filter feeders in the Bay of Kotor INTRODUCTION Environmental monitoring is
More informationInternational Journal of Innovative Research in Engineering Science and Technology APRIL 2018 ISSN X
HIGH DYNAMIC RANGE OF MULTISPECTRAL ACQUISITION USING SPATIAL IMAGES 1 M.Kavitha, M.Tech., 2 N.Kannan, M.E., and 3 S.Dharanya, M.E., 1 Assistant Professor/ CSE, Dhirajlal Gandhi College of Technology,
More informationSTRIPING NOISE REMOVAL OF IMAGES ACQUIRED BY CBERS 2 CCD CAMERA SENSOR
STRIPING NOISE REMOVAL OF IMAGES ACQUIRED BY CBERS 2 CCD CAMERA SENSOR a E. Amraei a, M. R. Mobasheri b MSc. Electrical Engineering department, Khavaran Higher Education Institute, erfan.amraei7175@gmail.com
More informationUsing Freely Available. Remote Sensing to Create a More Powerful GIS
Using Freely Available Government Data and Remote Sensing to Create a More Powerful GIS All rights reserved. ENVI, E3De, IAS, and IDL are trademarks of Exelis, Inc. All other marks are the property of
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 informationHow to Access Imagery and Carry Out Remote Sensing Analysis Using Landsat Data in a Browser
How to Access Imagery and Carry Out Remote Sensing Analysis Using Landsat Data in a Browser Including Introduction to Remote Sensing Concepts Based on: igett Remote Sensing Concept Modules and GeoTech
More informationRADAR (RAdio Detection And Ranging)
RADAR (RAdio Detection And Ranging) CLASSIFICATION OF NONPHOTOGRAPHIC REMOTE SENSORS PASSIVE ACTIVE DIGITAL CAMERA THERMAL (e.g. TIMS) VIDEO CAMERA MULTI- SPECTRAL SCANNERS VISIBLE & NIR MICROWAVE Real
More informationSaturation And Value Modulation (SVM): A New Method For Integrating Color And Grayscale Imagery
87 Saturation And Value Modulation (SVM): A New Method For Integrating Color And Grayscale Imagery By David W. Viljoen 1 and Jeff R. Harris 2 Geological Survey of Canada 615 Booth St. Ottawa, ON, K1A 0E9
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 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 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 informationIMAGE ENHANCEMENT - POINT PROCESSING
1 IMAGE ENHANCEMENT - POINT PROCESSING KOM3212 Image Processing in Industrial Systems Some of the contents are adopted from R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd edition, Prentice
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 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 informationApply Colour Sequences to Enhance Filter Results. Operations. What Do I Need? Filter
Apply Colour Sequences to Enhance Filter Results Operations What Do I Need? Filter Single band images from the SPOT and Landsat platforms can sometimes appear flat (i.e., they are low contrast images).
More informationDigital Image Processing - A Remote Sensing Perspective
ISSN 2278 0211 (Online) Digital Image Processing - A Remote Sensing Perspective D.Sarala Department of Physics & Electronics St. Ann s College for Women, Mehdipatnam, Hyderabad, India Sunita Jacob Head,
More informationWhite Paper. Medium Resolution Images and Clutter From Landsat 7 Sources. Pierre Missud
White Paper Medium Resolution Images and Clutter From Landsat 7 Sources Pierre Missud Medium Resolution Images and Clutter From Landsat7 Sources Page 2 of 5 Introduction Space technologies have long been
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 informationLesson 3: Working with Landsat Data
Lesson 3: Working with Landsat Data Lesson Description The Landsat Program is the longest-running and most extensive collection of satellite imagery for Earth. These datasets are global in scale, continuously
More informationPlanet Labs Inc 2017 Page 2
SKYSAT IMAGERY PRODUCT SPECIFICATION: ORTHO SCENE LAST UPDATED JUNE 2017 SALES@PLANET.COM PLANET.COM Disclaimer This document is designed as a general guideline for customers interested in acquiring Planet
More informationMultispectral Fusion for Synthetic Aperture Radar (SAR) Image Based Framelet Transform
Radar (SAR) Image Based Transform Department of Electrical and Electronic Engineering, University of Technology email: Mohammed_miry@yahoo.Com Received: 10/1/011 Accepted: 9 /3/011 Abstract-The technique
More informationMULTI-TEMPORAL IMAGE ANALYSIS OF THE COASTAL WATERSHED, NH INTRODUCTION
MULTI-TEMPORAL IMAGE ANALYSIS OF THE COASTAL WATERSHED, NH Meghan Graham MacLean, PhD Student Alexis M. Rudko, MS Student Dr. Russell G. Congalton, Professor Department of Natural Resources and the Environment
More information1. Introduction 2. Tectonics of NE Iceland Krafla rifting crisis (constraints from spy image matching)
1. Introduction 2. Tectonics of NE Iceland 3. 1975-1984 Krafla rifting crisis (constraints from spy image matching) 4. 1975-1984 Krafla rifting crisis (constraints from aerial photos) 5. Conclusions Tuesday
More informationRemote Sensing Platforms
Remote Sensing Platforms Remote Sensing Platforms - Introduction Allow observer and/or sensor to be above the target/phenomena of interest Two primary categories Aircraft Spacecraft Each type offers different
More informationDigital Photogrammetry. Presented by: Dr. Hamid Ebadi
Digital Photogrammetry Presented by: Dr. Hamid Ebadi Background First Generation Analog Photogrammetry Analytical Photogrammetry Digital Photogrammetry Photogrammetric Generations 2000 digital photogrammetry
More informationFiles Used in This Tutorial. Background. Calibrating Images Tutorial
In this tutorial, you will calibrate a QuickBird Level-1 image to spectral radiance and reflectance while learning about the various metadata fields that ENVI uses to perform calibration. This tutorial
More informationIntroduction to Remote Sensing
Introduction to Remote Sensing Spatial, spectral, temporal resolutions Image display alternatives Vegetation Indices Image classifications Image change detections Accuracy assessment Satellites & Air-Photos
More informationWetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis.
Wetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis. Update on current wetlands research in GISAG Nathan Torbick Spring 2003 Component One Remote
More informationERDAS IMAGINE Suite Comparison
ERDAS Suite Comparison A brief comparison of Essentials, Advantage and Professional age 1 of 7 Overview This document provides a brief comparison of the main features and capabilities found within the
More informationILLUMINATION CORRECTION OF LANDSAT TM DATA IN SOUTH EAST NSW
ILLUMINATION CORRECTION OF LANDSAT TM DATA IN SOUTH EAST NSW Elizabeth Roslyn McDonald 1, Xiaoliang Wu 2, Peter Caccetta 2 and Norm Campbell 2 1 Environmental Resources Information Network (ERIN), Department
More informationSUPER RESOLUTION INTRODUCTION
SUPER RESOLUTION Jnanavardhini - Online MultiDisciplinary Research Journal Ms. Amalorpavam.G Assistant Professor, Department of Computer Sciences, Sambhram Academy of Management. Studies, Bangalore Abstract:-
More informationCLASSIFICATION OF VEGETATION AREA FROM SATELLITE IMAGES USING IMAGE PROCESSING TECHNIQUES ABSTRACT
CLASSIFICATION OF VEGETATION AREA FROM SATELLITE IMAGES USING IMAGE PROCESSING TECHNIQUES Arpita Pandya Research Scholar, Computer Science, Rai University, Ahmedabad Dr. Priya R. Swaminarayan Professor
More informationIn late April of 1986 a nuclear accident damaged a reactor at the Chernobyl nuclear
CHERNOBYL NUCLEAR POWER PLANT ACCIDENT Long Term Effects on Land Use Patterns Project Introduction: In late April of 1986 a nuclear accident damaged a reactor at the Chernobyl nuclear power plant in Ukraine.
More informationGEOG432: Remote sensing Lab 3 Unsupervised classification
GEOG432: Remote sensing Lab 3 Unsupervised classification Goal: This lab involves identifying land cover types by using agorithms to identify pixels with similar Digital Numbers (DN) and spectral signatures
More informationREMOTE SENSING. Topic 10 Fundamentals of Digital Multispectral Remote Sensing MULTISPECTRAL SCANNERS MULTISPECTRAL SCANNERS
REMOTE SENSING Topic 10 Fundamentals of Digital Multispectral Remote Sensing Chapter 5: Lillesand and Keifer Chapter 6: Avery and Berlin MULTISPECTRAL SCANNERS Record EMR in a number of discrete portions
More informationCSC 170 Introduction to Computers and Their Applications. Lecture #3 Digital Graphics and Video Basics. Bitmap Basics
CSC 170 Introduction to Computers and Their Applications Lecture #3 Digital Graphics and Video Basics Bitmap Basics As digital devices gained the ability to display images, two types of computer graphics
More information* Tokai University Research and Information Center
Effects of tial Resolution to Accuracies for t HRV and Classification ta Haruhisa SH Kiyonari i KASA+, uji, and Toshibumi * Tokai University Research and nformation Center 2-28-4 Tomigaya, Shi, T 151,
More informationAPPLICATION OF COMPUTER VISION FOR DETERMINATION OF SYMMETRICAL OBJECT POSITION IN THREE DIMENSIONAL SPACE
APPLICATION OF COMPUTER VISION FOR DETERMINATION OF SYMMETRICAL OBJECT POSITION IN THREE DIMENSIONAL SPACE Najirah Umar 1 1 Jurusan Teknik Informatika, STMIK Handayani Makassar Email : najirah_stmikh@yahoo.com
More informationColor and More. Color basics
Color and More In this lesson, you'll evaluate an image in terms of its overall tonal range (lightness, darkness, and contrast), its overall balance of color, and its overall appearance for areas that
More informationLANDSAT-SPOT DIGITAL IMAGES INTEGRATION USING GEOSTATISTICAL COSIMULATION TECHNIQUES
LANDSAT-SPOT DIGITAL IMAGES INTEGRATION USING GEOSTATISTICAL COSIMULATION TECHNIQUES J. Delgado a,*, A. Soares b, J. Carvalho b a Cartographical, Geodetical and Photogrammetric Engineering Dept., University
More informationStatistics, Probability and Noise
Statistics, Probability and Noise Claudia Feregrino-Uribe & Alicia Morales-Reyes Original material: Rene Cumplido Autumn 2015, CCC-INAOE Contents Signal and graph terminology Mean and standard deviation
More informationFUNDAMENTALS OF DIGITAL IMAGES
FUNDAMENTALS OF DIGITAL IMAGES Lecture Image Data Structures Common Data Structures to Store Multiband Data BIL band interleaved by line BSQ band sequential BIP band interleaved by pixel Example Band Band
More informationCHARACTERISTICS OF REMOTELY SENSED IMAGERY. Spatial Resolution
CHARACTERISTICS OF REMOTELY SENSED IMAGERY Spatial Resolution There are a number of ways in which images can differ. One set of important differences relate to the various resolutions that images express.
More informationUSE OF HISTOGRAM EQUALIZATION IN IMAGE PROCESSING FOR IMAGE ENHANCEMENT
USE OF HISTOGRAM EQUALIZATION IN IMAGE PROCESSING FOR IMAGE ENHANCEMENT Sapana S. Bagade M.E,Computer Engineering, Sipna s C.O.E.T,Amravati, Amravati,India sapana.bagade@gmail.com Vijaya K. Shandilya Assistant
More informationAutomatic processing to restore data of MODIS band 6
Automatic processing to restore data of MODIS band 6 --Final Project for ECE 533 Abstract An automatic processing to restore data of MODIS band 6 is introduced. For each granule of MODIS data, 6% of the
More informationUse of digital aerial camera images to detect damage to an expressway following an earthquake
Use of digital aerial camera images to detect damage to an expressway following an earthquake Yoshihisa Maruyama & Fumio Yamazaki Department of Urban Environment Systems, Chiba University, Chiba, Japan.
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 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 informationGround Truth for Calibrating Optical Imagery to Reflectance
Visual Information Solutions Ground Truth for Calibrating Optical Imagery to Reflectance The by: Thomas Harris Whitepaper Introduction: Atmospheric Effects on Optical Imagery Remote sensing of the Earth
More informationWhat is Photogrammetry
Photogrammetry What is Photogrammetry Photogrammetry is the art and science of making accurate measurements by means of aerial photography: Analog photogrammetry (using films: hard-copy photos) Digital
More information