TESTFIELD TRENTO: GEOMETRIC EVALUATION OF VERY HIGH RESOLUTION SATELLITE IMAGERY

Similar documents
EVALUATION OF PLEIADES-1A TRIPLET ON TRENTO TESTFIELD

LPIS Orthoimagery An assessment of the Bing imagery for LPIS purpose

DEM GENERATION WITH WORLDVIEW-2 IMAGES

9/13/2011. Training Course Remote Sensing Basic Theory & Image Processing Methods September 2011

Geometric potential of Pleiades models with small base length

RADIOMETRIC AND GEOMETRIC CHARACTERISTICS OF PLEIADES IMAGES

Monitoring the vegetation success of a rehabilitated mine site using multispectral UAV imagery. Tim Whiteside & Renée Bartolo, eriss

UltraCam and UltraMap Towards All in One Solution by Photogrammetry

Phase One 190MP Aerial System

LAST GENERATION UAV-BASED MULTI- SPECTRAL CAMERA FOR AGRICULTURAL DATA ACQUISITION

CHARACTERISTICS OF VERY HIGH RESOLUTION OPTICAL SATELLITES FOR TOPOGRAPHIC MAPPING

Leica ADS80 - Digital Airborne Imaging Solution NAIP, Salt Lake City 4 December 2008

Advanced Optical Satellite (ALOS-3) Overviews

Topographic mapping from space K. Jacobsen*, G. Büyüksalih**

HIGH RESOLUTION COLOR IMAGERY FOR ORTHOMAPS AND REMOTE SENSING. Author: Peter Fricker Director Product Management Image Sensors

Comparing geometric and radiometric information from GeoEye-1 and WorldView-2 multispectral imagery

Summary of the VHR image acquisition Campaign 2014 and new sensors for 2015

GEO 428: DEMs from GPS, Imagery, & Lidar Tuesday, September 11

Radiometric Comparison between GeoEye-1 and WorldView-2 Panchromatic and Multispectral Imagery

Tutorial 10 Information extraction from high resolution optical satellite sensors

INFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE

School of Rural and Surveying Engineering National Technical University of Athens

ENVI Orthorectification Module

An Introduction to Geomatics. Prepared by: Dr. Maher A. El-Hallaq خاص بطلبة مساق مقدمة في علم. Associate Professor of Surveying IUG

Processing of stereo scanner: from stereo plotter to pixel factory

Leica - 3 rd Generation Airborne Digital Sensors Features / Benefits for Remote Sensing & Environmental Applications

Introduction to Remote Sensing Fundamentals of Satellite Remote Sensing. Mads Olander Rasmussen

Phase One ixu-rs1000 Accuracy Assessment Report Yu. Raizman, PhaseOne.Industrial, Israel

VisionMap A3 Edge A Single Camera for Multiple Solutions

Introduction to WG5 on CwRS imagery use and alternatives and QE5 on claimed rate inside the RP Peter Viskum Jørgensen, FERV and Birger Faurholt

The Most Suitable Sizes Of Ground Control Points (Gcps) For World View2

Fusion of Heterogeneous Multisensor Data

DigitalGlobe High Resolution Satellite Imagery

Flood modelling and management. Glasgow University. 8 September Paul Shaw - GeoVision

ROLE OF SATELLITE DATA APPLICATION IN CADASTRAL MAP AND DIGITIZATION OF LAND RECORDS DR.T. RAVISANKAR GROUP HEAD (LRUMG) RSAA/NRSC/ISRO /DOS HYDERABAD

TechTime New Mapping Tools for Transportation Engineering

WorldView-2. WorldView-2 Overview

Assessment of Unmanned Aerial Vehicle for Management of Disaster Information

LECTURE NOTES 2016 CONTENTS. Sensors and Platforms for Acquisition of Aerial and Satellite Image Data

DEMS BASED ON SPACE IMAGES VERSUS SRTM HEIGHT MODELS. Karsten Jacobsen. University of Hannover, Germany

E m e r g e n c y M a n a g e m e n t S e r v i c e. C o p e r n i c u s A e r i a l c o m p o n e n t s t a t u s s t u d y

Role, Workflow and Challenges

ENVI Orthorectification Module

CALIBRATION OF OPTICAL SATELLITE SENSORS

Airborne or Spaceborne Images for Topographic Mapping?

CALIBRATING THE NEW ULTRACAM OSPREY OBLIQUE AERIAL SENSOR Michael Gruber, Wolfgang Walcher

Tutorial 10 Information extraction from high resolution optical satellite sensors

Overview. Objectives. The ultimate goal is to compare the performance that different equipment offers us in a photogrammetric flight.

MSB Imagery Program FAQ v1

RPAS Photogrammetric Mapping Workflow and Accuracy

Aerial Triangulation Radiometry Essentials Dense Matching Ortho Generation

Aerial photography: Principles. Frame capture sensors: Analog film and digital cameras

NAVIGATION AND REMOTE SENSING PAYLOADS AND METHODS OF THE SARVANT UNMANNED AERIAL SYSTEM

How to get base geospatial data for SDI from high resolution satellite images

EnsoMOSAIC Aerial mapping tools

CHARACTERISTICS OF REMOTELY SENSED IMAGERY. Spatial Resolution

European Space Imaging

News on Image Acquisition for Campaign 2008

CALIBRATION OF IMAGING SATELLITE SENSORS

Section 2 Image quality, radiometric analysis, preprocessing

RECENT DEVELOPMENTS OF DIGITAL CAMERAS AND SPACE IMAGERY. Karsten JACOBSEN

Module 3 Introduction to GIS. Lecture 8 GIS data acquisition

Satellite Data Requirements - Copernicus Security Requirements focused on Support to EU External Actions

Planet Labs Inc 2017 Page 2

IMAGE DATA AND TEST FIELD

EVALUATION OF CAPABILITIES OF FUZZY LOGIC CLASSIFICATION OF DIFFERENT KIND OF DATA

to Geospatial Technologies

High Resolution Sensor Test Comparison with SPOT, KFA1000, KVR1000, IRS-1C and DPA in Lower Saxony

SPOT6. Impact of Spot 6 and 7 in the Constitution and Update of Spatial Data Infrastructures over Africa

remote sensing? What are the remote sensing principles behind these Definition

Advanced Techniques in Urban Remote Sensing

Geometry of Aerial Photographs

THREE-DIMENSIONAL MAPPING USING BOTH AIRBORNE AND SPACEBORNE IFSAR TECHNOLOGIES ABSTRACT INTRODUCTION

PROPERTY OF THE LARGE FORMAT DIGITAL AERIAL CAMERA DMC II

EXAMPLES OF TOPOGRAPHIC MAPS PRODUCED FROM SPACE AND ACHIEVED ACCURACY CARAVAN Workshop on Mapping from Space, Phnom Penh, June 2000

European Space Imaging. Your Partner for Very High-Resolution Satellite Imagery GEOGRAPHIC

Abstract Quickbird Vs Aerial photos in identifying man-made objects

Unmanned Aerial Vehicle Data Acquisition for Damage Assessment in. Hurricane Events

Configuration, Capabilities, Limitations, and Examples

[GEOMETRIC CORRECTION, ORTHORECTIFICATION AND MOSAICKING]

Automatic geo-registration of satellite imagery

What can we check with VHR Pan and HR multispectral imagery?

Unmanned Aerial Vehicles: A New Approach for Coastal Habitat Assessment

SAR Imagery: Airborne or Spaceborne? Presenter: M. Lorraine Tighe PhD

VisionMap Sensors and Processing Roadmap

Jens Kremer ISPRS Hannover Workshop 2017,

System data analysis of Greek sites

COMPARISON OF INFORMATION CONTENTS OF HIGH RESOLUTION SPACE IMAGES


Acquisition of Aerial Photographs and/or Satellite Imagery

SUGARCANE GROUND REFERENCE DATA OVER FOUR FIELDS IN SÃO PAULO STATE

Update on UltraCam and UltraMap technology

ASPECTS OF DEM GENERATION FROM UAS IMAGERY

UltraCam and UltraMap An Update

Application and potentials of RADAR and LiDAR technologies for forest carbon assessment in Pacific Island Countries

Status of MOLI development MOLI (Multi-footprint Observation Lidar and Imager)

VERIFICATION OF POTENCY OF AERIAL DIGITAL OBLIQUE CAMERAS FOR AERIAL PHOTOGRAMMETRY IN JAPAN

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 2, No 3, 2012

Use of digital aerial camera images to detect damage to an expressway following an earthquake

High resolution satellite imagery a shared and collective data source

Transcription:

TESTFIELD TRENTO: GEOMETRIC EVALUATION OF VERY HIGH RESOLUTION SATELLITE IMAGERY G. AGUGIAROa, D. POLIb, F. REMONDINOa, 3DOM, 3D Optical Metrology Unit Bruno Kessler Foundation, Trento, Italy a b Vermessung AVT ZT-GmbH, Austria Melbourne, 28 August 2012 Motivation VHR optical satellite images are used increasingly in the scientific and civil community (e.g. for settlements analysis, disaster risk assessment, postdisaster needs assessment, etc.) DSMs are a crucial for the extraction of 3D information (e.g. building and city models, terrain geomorphologic parameters, canopy height, geologic mass monitoring, etc.) 3DOM is building a testfield over the city of Trento (Northern Italy), which comprises also 2 stereo-pairs (one GeoEye-1 and one WorldView-2) This talk presents the testfield and focuses on 3D data extraction from satellite imagery. 1

Area extents: ca. 15x15 km Min height: 150 m a.s.l. Max height: 2181 m a.s.l. Trento Population: 115000 inh. Height: ca. 200 m a.s.l. Spaceborne datasets: Sensor Acquisition date Bands Quickbird 30 Oct 2005 MS4 Quickbird 17 Jul 2006 SPOT5-VEG SPOT5-HRG 31 Aug 2006 31 Aug 2006 WV2 22 Aug 2010 WV2 22 Aug 2010 GE1 28 Sep 2011 GE1 28 Sep 2011 MS4 MS4 MS8 MS8 MS4 MS4 Avg. GSD [m] 0.6 2.4 0.6 2.4 10.0 2.5 0.51 2.0 0.51 2.0 0.50 2.0 0.50 2.0 Size x [km] Size y [km] Licensee 14,75 7,51 Univ. Trento 15,08 9,20 Univ. Trento 36,28 36,22 36,13 36,09 Univ. Trento Univ. Trento 17,64 17,64 FBK 17,64 17,64 FBK 10,00 10,00 FBK 10,00 10,00 FBK 2

Other datasets: A raster-based DSM/DTM, derived from a flight in 2006/7 at ca. 1.3 points/m2. Cell size 1 m (2 m on mountainous areas). σz,dsm=15 cm, σ z,dtm=30 cm. Reference dataset for the geometric analyses. 230 digital orthophotos (2006 and 2009), GSD 50 cm. 10 nadir aerial images (2009), acquired by RMKTOP15 analogic camera over the city of Trento (overlap: 60% along track, 30% across track). GSD 12 cm. 180 UAV images (2012), acquired by a Microdrones MD4-200 mounting a Pentax Optio A40 digital camera (overlap: 80% along track, 40% across track). GSD 3 cm 70 ground points (2011), acq. by GNSS, sub-decimetre accuracy 12 ground points (2012), acq. by total station, cm accuracy Space- and airborne imagery overview WorldView-2 GeoEye-1 Quickbird, Quickbird Aerial images (white) UAV images SPOT (not depicted, larger area) 3

GCPs WorldView-2 (WV2) stereo-pair: Acquisition on: 22 Aug 2010, 10:40 am (GMT) + MS8 (R, G, B, NIR, coastal, yellow, red edge, and NIR-2), avg GSD 0.5 m (), 2 m (MS) Extents: 17.64x17.64 km, overlap 100% Processing level: Stereo 1B Provided with RPCs FS, in-track view. angle 15.9, RS, -14.0 Cloud cover <10% GeoEye-1 (GE1) stereo-pair: Acquisition on: 8 Sep 2011, 10:20 am (GMT) + MS4 avg GSD 0.5 m (), 2 m (MS) Extents: 10x10 km, overlap 100% Processing level: GeoStereo Provided with RPCs RS, in-track view. angle 15, RS, -20 Cloud cover <4% 4

Example of artefacts found in WV-2 images Examples of artefacts found in GE-1 images Methodology General workflow: Stereo image acquisition Image orientation Image matching DSM generation Photogrammetry Orthorectification DTM extraction Building heights Building extraction Visualisation 5

Methodology Image orientation and DSM extraction Carried out within Sat-PP (4Dixplorer) Image orientation by RPCs + GCPs (sub-pixel accuracy) DSM generated a 1 m (GSD x2) Few tie points manually measured at height discontinuities No further editing/filtering In general: DSM succesfully generated from both stereo pairs In mountain areas: valleys, mountain sides and ridges well modelled In urban areas: building agglomerations, blocks with different heights, road network, some infrastructures (i.e. bridges), and rivers are well outlined. In rural areas: adjacent fields, vegetation, and buildings are recognisable on flat or hilly terrain Wrong DSM extraction due to clouds in the lower bottom part Results DSM from GeoEye-1 6

Results DSM quality assessment (preliminary results) Both DSMs compared with the DSM 3 areas (Area1, Area2, Area3) Compute height differences and statistics within areas (min, max, avg, stddev, RMSE) Area1 Historical city centre. Completely built up area, irregular building shapes, narrow streets, nearly no vegetation Area2 Railway station area. Large regular buildings, larger streets with some trees Area3 Residential area. Regular geometries, courtyards and streets with trees. Results: Area1 GE1 (all values in metres) min: -44.4, max: 26.5 sigma: 6.6 avg: 2.3 RMSE: 6.9 Error distribution: GE1, WV2 WV2 min: -53.9, max: 26.7 avg: 1.9 sigma: 7.1 RMSE: 7.4 7

Results: Area1 Profiles: Lidar, GE1, WV2 Comments: Roof height estimated quite well Errors between buildings: narrow streets not visible in the stereo-pairs due to shadows or occlusions Two churches not reconstructed: homogeneous roof material? GE1 DSM slightly more accurate than the WV2 one Results: Area2 GE1 Error distribution: GE1, WV2 WV2 (all values in metres) min: -28.1, max: 53.5 sigma: 6.9 avg: 1.7 RMSE: 7.1 min: -39.0, max: 58.4 avg: 0.7 sigma: 7.1 RMSE: 7.4 8

Results: Area2 Profiles: Lidar, GE1, WV2 Comments: General agreement between and GE1/WV2 Some differences due to vegetation One building demolished GE1 DSM slightly more accurate than the WV2 one Results: Area3 GE1 Error distribution: GE1, WV2 WV2 (all values in metres) min: -46.3, max: 28.8 sigma: 7.4 avg: 0.5 RMSE: 7.9 min: -43.7, max: 27.7 avg: 0.3 sigma: 8.5 RMSE: 8.5 9

Results: Area3 Profiles: Lidar, GE1, WV2 Comments: General agreement between and GE1/WV2 Significant height differences between tall buildings GE1 DSM slightly more accurate than the WV2 one Results: Area3 Example of occlusion between tall buildings in GE1 stereo-pair: forward view [left] and backward view [right]. 10

Conclusions and outlook Conclusions: : data acquisition completed Wrt. VHR spaceborne optical imagery, preliminary results show that: GE1/WV2 DSMs model flat, hilly and mountainous areas well (cliffs, ridges and roads are well defined) In urban areas, building blocks are recognised in the historic city centre, individual buildings are visible in residential and industrial areas Problematic areas: Narrow streets, tall nearby buildings Homogeneous/poor texture (shadows, roof covers) Presence of vegetation On the three test areas: the performance of GE1 and WV2 sensors is similar Outlook: Wrt. GE1/WV2 imagery, more extensive tests are planned Radiometric investigations DSM generation with other packages More accurate DSM analyses (e.g. masking out temporal changes) Thank you for your attention Contact: Giorgio Agugiaro Bruno Kessler Foundation 3DOM Via Sommarive, 18-38123 Trento - Italy E-mail: agugiaro@fbk.eu - Tel. +39 0461 314913 Credits: Daniela Poli, Vermessung AVT ZT-GmbH, Austria Fabio Remondino, FBK-3DOM, Italy Provincia Autonoma di Trento, Italy 3M project (co-founded Marie-Curie Actions FP7 PCOFOUND GA-2008-226070, acronym Trentino Project ) 11