BUILT-UP AREAS MAPPING AT GLOBAL SCALE BASED ON ADAPATIVE PARAMETRIC THRESHOLDING OF SENTINEL-1 INTENSITY & COHERENCE TIME SERIES
|
|
- Georgina Thompson
- 6 years ago
- Views:
Transcription
1 BUILT-UP AREAS MAPPING AT GLOBAL SCALE BASED ON ADAPATIVE PARAMETRIC THRESHOLDING OF SENTINEL-1 INTENSITY & COHERENCE TIME SERIES M. Chini, R. Pelich, R. Hostache, P. Matgen MultiTemp 2017 June 27-29, Bruges (Belgium)
2 WORKING HYPOTHESIS I. Building SAR backscattering is high. II. InSAR coherence in urban areas is high. SAR Intensity InSAR coherence
3 WORKING HYPOTHESIS Urban areas appear very bright (very high backscattering). Smooth water surface covering the terrain reflects the radar signal in the specular direction (very low backscatter). Number of pixels Backscattering [db]
4 CLASSIFICATION STRATEGY SAR intensity Statistical based algorithms typically parameterize distribution functions to assign pixels to 2 or more semantic classes of interest. Classes of interest often represent only a small fraction of an entire SAR scene: difficulty in parameterizing such distributions functions. A sufficiently high percentage of pixels is typically required to estimate a reliable and robust distribution function that can be used for accurate detection of land cover class. Thus, the capability to detect classes cannot only rely on spectral signatures but also on the prior probability of the target class.
5 CLASSIFICATION STRATEGY We propose a Hierarchical Split-Based Approach (HSBA) where the size of tiles is not fixed a priori, already developed to map flooded areas. The algorithm has been developed in the framework of the Urban Round- Robin exercise, supported by the European Space Agency (ESA) through the ESA Land Cover Climate Change Initiative (CCI), and tested on Sentinel-1 data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. M. Chini, R. Hostache, L. Giustarini, P. Matgen, A Hierarchical Split-Based Approach (HSBA) for parametric thresholding of SAR images: flood inundation as a test case, IEEE Transactions on Geoscience and Remote Sensing (Under review).
6 CLASSIFICATION STRATEGY HSBA We assume that the image is composed of three main classes (Urban, Water and Other Classes) We have adopted a two-steps statistical approach: First, we apply the algorithm to delineate the water class (HSBA-Water) Second, we apply the algorithm to identify the urban areas (HSBA- Urban) in the remaining image where water pixels have been masked out The algorithm delineating Water or Urban areas is composed of two steps: Hierarchical Split-Based Approach (HSBA) to select bimodal areas Based on the statistics of the selected bimodal areas, we use a hybrid methodology, which combines distributions fitting, thresholding and region growing, for the automatic detection of the class of interest in the entire scene
7 CLASSIFICATION STRATEGY HSBA SAR image L 0 L 1 L 2 L 3
8 CLASSIFICATION STRATEGY HSBA Class distributions of SAR images are assumed Gaussian: (yy μμ 1 ) 2 h yy = GG 1 yy + GG 2 (yy) = AA 1 ee 2ssss2 1 (yy μμ 2 ) 2 + AA 2 ee 2ssss2 To fit the distributions the Levenberg-Marquardt algorithm is used Rules to select tiles: 1) Tile histogram is bimodal, Ashman D > 2 AAAA (h (yy)) = 2 μμ 1 μμ 2 ssss ssss 2 2) Smallest class is more that 10% of the tile.
9 CLASSIFICATION STRATEGY Region Growing The selection of the threshold can benefit from the combination of the contextual information of the image with its intensity information. Here, we use a region growing approach that assumes that pixels constituting the target class are clustered rather than randomly spread out over the entire image.
10 SAR IMAGING MECHANISM Double-bounce & Foreshortening Foreshortening Double-Bounce θ Double- Bounce φ Range direction Azimuth direction Higher is θ, higher is the double-bounce backscattering. Lower is φ, higher is the double-bounce backscattering.
11 TEMPORAL FEATURES Intensity & coherence Temporal Average Intensity (TAI) To reduce speckle without reducing the spatial resolution. One image per month (one year of acquisitions). Temporal Average Coherence (TAC) It is extracted form InSAR coherence maps with one month temporal baseline (one year of acquisitions). Purpose: catch vegetation changes during different seasons.
12 OVER AND UNDER DETECTION Using only SAR intensity Over detection: Vegetated areas Solution: InSAR coherence Foreshortening in mountainous areas Solution: DEM to extract local incidence angle Under detection: Unfavorable Line of Sight (Los) Solution: Ascending and descending orbit paths
13 OVER DETECTION Vegetated areas Intensity Egypt Intensity Coherence Coherence
14 OVER DETECTION Vegetated areas Intensity HSBA Coherence Intensity HSBA Coherence
15 OVER DETECTION Foreshortening (FS) in mountainous areas FS CC removal filtering HSBA Coherence Intensity
16 UNDER DETECTION Unfavorable Line of Sight Descending Building map / DESC Ascending Building map / ASC
17 PROCESSING CHAIN 1) Temporal Average of Intensity and Coherence extraction 2) Foreshortening removal from TAI 3) Water classification (HSBA) and removal from TAI 4) Buildings classification by HSBA 5) Over detection (vegetation) removal using TAC thresholding 6) Building maps from ascending and descending orbit merge
18 TEST CASES Areas of interest: Portugal, Sicily (Italy) and Egypt One image per month over one year of acquisitions
19 RESULTS Egypt land cover cci built-up areas. proposed method built-up areas.
20 RESULTS Egypt complementarity of the building maps obtained from the ascending and descending orbits. areas classified as building within both image tracks. areas classified as building in one image track SAR Intensity Buildings map
21 RESULTS Egypt
22 RESULTS Portugal degree of imperviousness 2012 proposed method built-up areas
23 RESULTS Portugal SAR Intensity Proposed method buildings map Degree of imperviousness 2012, Copernicus Land Monitoring Service
24 RESULTS Sicily (Italy) degree of imperviousness 2012 proposed method built-up areas Greenhouses
25 CONCLUSIONS This procedure renders the urban mapping approach independent of the different technical characteristics of the SAR scene (e.g. spatial resolution or percentage of urban extension respect to extension of the image). The presumed hypotheses are rather simple but valid for almost all buildings in our test cases. Perform an in-depth evaluation of our methodology: a comparison with the building map developed by the DLR s Global Urban Footprint (GUF) project.
URBAN FOOTPRINT MAPPING WITH SENTINEL-1 DATA
URBAN FOOTPRINT MAPPING WITH SENTINEL-1 DATA Data: Sentinel-1A IW SLC 1SSV: S1A_IW_SLC 1SSV_20160102T005143_20160102T005208_009308_00D72A_849D S1A_IW_SLC 1SSV_20160126T005142_20160126T005207_009658_00E14A_49C0
More informationInterferometric Evaluation of Sentinel-1A TOPS data
Interferometric Evaluation of Sentinel-1A TOPS data N. Yague-Martinez, F. Rodriguez Gonzalez, R. Brcic, R. Shau Remote Sensing Technology Institute. DLR, Germany ESTEC/Contract No. 4000111074/14/NL/MP/lf
More informationFlood detection using radar data Basic principles
Flood detection using radar data Basic principles André Twele, Sandro Martinis and Jan-Peter Mund German Remote Sensing Data Center (DFD) 1 Overview Introduction Basic principles of flood detection using
More informationRepeat-pass SAR Interferometry Experiments with Gaofen-3: A Case Study of Ningbo Area
Repeat-pass SAR Interferometry Experiments with Gaofen-3: A Case Study of Ningbo Area Tao Zhang, Xiaolei Lv, Bing Han, Bin Lei and Jun Hong Key Laboratory of Technology in Geo-spatial Information Processing
More informationMission Status and Data Availability: TanDEM-X
Mission Status and Data Availability: TanDEM-X Irena Hajnsek, Thomas Busche, Alberto Moreira & TanDEM-X Team Microwaves and Radar Institute, German Aerospace Center irena.hajnsek@dlr.de 26-Jan-2009 Outline
More informationInterferometry Module for Digital Elevation Model Generation
Interferometry Module for Digital Elevation Model Generation In order to fully exploit processes of the Interferometry Module for Digital Elevation Model generation, the European Space Agency (ESA) has
More informationSentinel-1 Toolbox. TOPS Interferometry Tutorial Issued May 2014
Sentinel-1 Toolbox TOPS Interferometry Tutorial Issued May 2014 Copyright 2015 Array Systems Computing Inc. http://www.array.ca/ https://sentinel.esa.int/web/sentinel/toolboxes Interferometry Tutorial
More informationAUTOMATIC INTERPRETATION OF HIGH RESOLUTION SAR IMAGES: FIRST RESULTS OF SAR IMAGE SIMULATION FOR SINGLE BUILDINGS
AUTOMATIC INTERPRETATION OF HIGH RESOLUTION SAR IMAGES: FIRST RESULTS OF SAR IMAGE SIMULATION FOR SINGLE BUILDINGS J. Tao *, G. Palubinskas, P. Reinartz German Aerospace Center DLR, 82234 Oberpfaffenhofen,
More informationLateral Ground Movement Estimation from Space borne Radar by Differential Interferometry.
Lateral Ground Movement Estimation from Space borne Radar by Differential Interferometry. Abstract S.Sircar 1, 2, C.Randell 1, D.Power 1, J.Youden 1, E.Gill 2 and P.Han 1 Remote Sensing Group C-CORE 1
More informationSynthetic Aperture Radar (SAR) Polarimetry for Wetland Mapping & Change Detection
Synthetic Aperture Radar (SAR) Polarimetry for Wetland Mapping & Change Detection Jennifer M. Corcoran, M.S. Remote Sensing & Geospatial Analysis Laboratory Natural Resource Science & Management PhD Program
More informationALOS PALSAR. Orthorectification Tutorial Issued March 2015 Updated August Luis Veci
ALOS PALSAR Orthorectification Tutorial Issued March 2015 Updated August 2016 Luis Veci Copyright 2015 Array Systems Computing Inc. http://www.array.ca/ http://step.esa.int ALOS PALSAR Orthorectification
More informationHydrological network detection for SWOT data. S. Lobry, F. Cao, R. Fjortoft, JM Nicolas, F. Tupin
Hydrological network detection for SWOT data S. Lobry, F. Cao, R. Fjortoft, JM Nicolas, F. Tupin IRS SPU avril 2016 SWOT mission Large water bodies detection Fine network detection Further works page 1
More informationContextual descriptors ad neural networks for scene analysis in VHR SAR images
Workshop Nazionale La Missione COSMO-SkyMed: Stato dell Arte, Applicazioni e Prospettive Future Roma, 13-15 Novembre 2017 Contextual descriptors ad neural networks for scene analysis in VHR SAR images
More informationFlood detection using TerraSAR-X data Hands-on tutorial
Flood detection using TerraSAR-X data Hands-on tutorial André Twele and Sandro Martinis German Remote Sensing Data Center (DFD) 1 Hands-on training and tutorial Flood classification using TerraSAR-X data
More informationInSAR Operational and Processing Steps for DEM Generation
InSAR Operational and Processing Steps for DEM Generation By F. I. Okeke Department of Geoinformatics and Surveying, University of Nigeria, Enugu Campus Tel: 2-80-5627286 Email:francisokeke@yahoo.com Promoting
More informationURBAN CHANGE DETECTION USING COHERENCE AND INTENSITY CHARACTERISTICS OF MULTI-TEMPORAL ERS-1/2 IMAGERY
URBAN CHANGE DETECTION USING COHERENCE AND INTENSITY CHARACTERISTICS OF MUTI-TEMPORA ERS-1/2 IMAGERY M. S. iao a, *,. M. Jiang a, H. in b, D. R. i a a IESMARS, Wuhan University, 129 uoyu Road, Wuhan, China-(liao,
More informationIce surface velocities using SAR
Ice surface velocities using SAR Thomas Schellenberger, PhD ESA Cryosphere Remote Sensing Training Course 2018 UNIS Longyearbyen, Svalbard 12 th June 2018 thomas.schellenberger@geo.uio.no Outline Synthetic
More informationSEA SURFACE SPEED FROM TERRASAR-X ATI DATA
SEA SURFACE SPEED FROM TERRASAR-X ATI DATA Matteo Soccorsi (1) and Susanne Lehner (1) (1) German Aerospace Center, Remote Sensing Technology Institute, 82234 Weßling, Germany, Email: matteo.soccorsi@dlr.de
More informationURBAN CLASSIFICATION WITH SENTINEL-1 Case Study: Germany, 2018
_p TRAINING KIT LAND06 URBAN CLASSIFICATION WITH SENTINEL-1 Case Study: Germany, 2018 Research and User Support for Sentinel Core Products The RUS Service is funded by the European Commission, managed
More informationSAR time series. JM Nicolas F. Tupin
SAR time series JM Nicolas F. Tupin Context Golden age of SAR sensors: improved spatial, polarimetric and temporal resolutions CSK TerraSAR-X Sentinel I RadarSAT-2 page 1 SAR sensors resolutions Polarimetric
More informationEstimation of building heights from high-resolution
DLR - IRIDeS - UN-SPIDER Joint Workshop on Remote Sensing and Multi-Risk Modeling for Disaster Management 19 and 20 September 2014 at UN-SPIDER Bonn Office Estimation of building heights from high-resolution
More informationWIDE AREA DEFORMATION MAP GENERATION WITH TERRASAR-X DATA: THE TOHOKU-OKI EARTHQUAKE 2011 CASE
WIDE AREA DEFORMATION MAP GENERATION WITH TERRASAR-X DATA: THE TOHOKU-OKI EARTHQUAKE 2011 CASE Nestor Yague-Martinez (1), Michael Eineder (2), Christian Minet (2), Birgitt Schättler (2) (1) Starlab Barcelona
More informationSentinel-1 InSAR AP workshop
Sentinel-1 InSAR AP workshop Sentinel-1 InSAR progress and experience at GAMMA U. Wegmüller, C. Werner, A. Wiesmann, T. Strozzi Gamma Remote Sensing AG - Progress made since S1A Expert Users meeting at
More informationEXPLOITING EARTH OBSERVATION DATA PRODUCTS FOR MAPPING LOCAL CLIMATE ZONES
EXPLOITING EARTH OBSERVATION DATA PRODUCTS FOR MAPPING LOCAL CLIMATE ZONES Zina Mitraka 1,3, Nektarios Chrysoulakis 1, Jean-Philippe Gastellu- Etchegorry 2, Fabio Del Frate 3 1 Foundation for Research
More informationCoherence Based Polarimetric SAR Tomography
I J C T A, 9(3), 2016, pp. 133-141 International Science Press Coherence Based Polarimetric SAR Tomography P. Saranya*, and K. Vani** Abstract: Synthetic Aperture Radar (SAR) three dimensional image provides
More informationON THE USE OF POINT TARGET CHARACTERISTICS IN THE ESTIMATION OF LOW SUBSIDENCE RATES DUE TO GAS EXTRACTION IN GRONINGEN, THE NETHERLANDS
ON THE USE OF POINT TARGET CHARACTERISTICS IN THE ESTIMATION OF LOW SUBSIDENCE RATES DUE TO GAS EXTRACTION IN GRONINGEN, THE NETHERLANDS ABSTRACT Gini Ketelaar (1), Freek van Leijen (1), Petar Marinkovic
More informationInterferometry Tutorial with RADARSAT-2 Issued March 2014 Last Update November 2017
Sentinel-1 Toolbox with RADARSAT-2 Issued March 2014 Last Update November 2017 Luis Veci Copyright 2015 Array Systems Computing Inc. http://www.array.ca/ http://step.esa.int with RADARSAT-2 The goal of
More informationPSI Precision, accuracy and validation aspects
PSI Precision, accuracy and validation aspects Urs Wegmüller Charles Werner Gamma Remote Sensing AG, Gümligen, Switzerland, wegmuller@gamma-rs.ch Contents Aim is to obtain a deeper understanding of what
More informationDETECTION AND QUANTIFICATION OF ROCK GLACIER. DEFORMATION USING ERS D-InSAR DATA
DETECTION AND QUANTIFICATION OF ROCK GLACIER DEFORMATION USING ERS D-InSAR DATA Lado W. Kenyi 1 and Viktor Kaufmann 2 1 Institute of Digital Image Processing, Joanneum Research Wastiangasse 6, A-8010 Graz,
More informationMULTI-TEMPORAL SAR DATA FILTERING FOR LAND APPLICATIONS. I i is the estimate of the local mean backscattering
MULTI-TEMPORAL SAR DATA FILTERING FOR LAND APPLICATIONS Urs Wegmüller (1), Maurizio Santoro (1), and Charles Werner (1) (1) Gamma Remote Sensing AG, Worbstrasse 225, CH-3073 Gümligen, Switzerland http://www.gamma-rs.ch,
More informationMULTI-TEMPORAL INTERFEROMETRIC POINT TARGET ANALYSIS
MULTI-TEMPORAL INTERFEROMETRIC POINT TARGET ANALYSIS U. WEGMÜLLER, C. WERNER, T. STROZZI, AND A. WIESMANN Gamma Remote Sensing AG. Thunstrasse 130, CH-3074 Muri (BE), Switzerland wegmuller@gamma-rs.ch,
More informationCalculation of beta naught and sigma naught for TerraSAR-X data
Calculation of beta naught and sigma naught for TerraSAR-X data 1 Introduction The present document describes the successive steps of the TerraSAR-X data absolute calibration. Absolute calibration allows
More informationCombination of GNSS and InSAR for Future Australian Datums
Combination of GNSS and InSAR for Future Australian Datums Thomas Fuhrmann, Matt Garthwaite, Sarah Lawrie, Nick Brown Interferometric Synthetic Aperture Radar Motivation Current situation Static Datum:
More informationSentinel-1 Toolbox. Interferometry Tutorial Issued March 2015 Updated August Luis Veci
Sentinel-1 Toolbox Interferometry Tutorial Issued March 2015 Updated August 2016 Luis Veci Copyright 2015 Array Systems Computing Inc. http://www.array.ca/ http://step.esa.int Interferometry Tutorial The
More informationARTIFICIAL SCATTERERS FOR S.A.R. INTERFEROMETRY
ARTIFICIAL SCATTERERS FOR S.A.R. INTERFEROMETRY Parizzi A. (1), Perissin D. (1), Prati C. (1), Rocca F. (1) (1) Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy ABSTRACT. The evaluation of land
More informationDo It Yourself 8. Polarization Coherence Tomography (P.C.T) Training Course
Do It Yourself 8 Polarization Coherence Tomography (P.C.T) Training Course 1 Objectives To provide a self taught introduction to Polarization Coherence Tomography (PCT) processing techniques to enable
More informationThe novel tool of Cumulative Discriminant Analysis applied to IASI cloud detection
Applied Spectroscopy The novel tool of Cumulative Discriminant Analysis applied to IASI cloud detection G. Masiello, C. Serio, S. Venafra, SI/UNIBAS, School of Engineering, University of Basilicata, Potenza,
More informationLONG-TERM SUBSIDENCE MONITORING OF CITY AREAS AT NORDIC LATITUDES USING ERS SAR DATA
LONG-TERM SUBSIDENCE MONITORING OF CITY AREAS AT NORDIC LATITUDES USING ERS SAR DATA Tom R. Lauknes (1,2), Geir Engen (1), Kjell A. Høgda (1), Inge Lauknes (1), Torbjørn Eltoft (2), Dan J. Weydahl (3)
More informationObject-Based Classification & ecognition. Zutao Ouyang 11/17/2015
Object-Based Classification & ecognition Zutao Ouyang 11/17/2015 What is Object-Based Classification The object based image analysis approach delineates segments of homogeneous image areas (i.e., objects)
More informationSignal Processing Laboratory
C.S.L Liege Science Park Avenue du Pré-Aily B-4031 ANGLEUR Belgium Tel: +32.4.382.46.00 Fax: +32.4.367.56.13 Signal Processing Laboratory Anne Orban VITO June 16, 2011 C. Barbier : the team Remote Sensing
More informationData: a collection of numbers or facts that require further processing before they are meaningful
Digital Image Classification Data vs. Information Data: a collection of numbers or facts that require further processing before they are meaningful Information: Derived knowledge from raw data. Something
More informationOperational process interferometric for the generation of a digital model of ground Applied to the couple of images ERS-1 ERS-2 to the area of Algiers
Operational process interferometric for the generation of a digital model of ground Applied to the couple of images ERS-1 ERS-2 to the area of Algiers F. Hocine, M.Ouarzeddine, A. elhadj-aissa,, M. elhadj-aissa,,
More informationTANDEM-X: DEM ACQUISITION IN THE THIRD YEAR ERA
TANDEM-X: DEM ACQUISITION IN THE THIRD YEAR ERA D. Borla Tridon, M. Bachmann, D. Schulze, C. J. Ortega Miguez, M. D. Polimeni, M. Martone and TanDEM-X Team Microwaves and Radar Institute, DLR 5 th International
More informationTarget recognition by means of spaceborne C-band SAR data
Target recognition by means of spaceborne C-band SAR data Daniele Perissin, Claudio Prati Dipartimento di Elettronica e Informazione POLIMI - Politecnico di Milano Milano, Italy daniele.perissin@polimi.it
More informationGABOR AND WEBER FEATURE EXTRACTION PERFORMANCE BASED ON URBAN ATLAS GROUND TRUTH
U.P.B. Sci. Bull., Series C, Vol. 78, Iss. 3, 2016 ISSN 2286-3540 GABOR AND WEBER FEATURE EXTRACTION PERFORMANCE BASED ON URBAN ATLAS GROUND TRUTH Mihaela STAN 1, Anca POPESCU 2, Dan Alexandru STOICHESCU
More informationGeometric and Radiometric Calibration of RADARSAT Images. David Small, Francesco Holecz, Erich Meier, Daniel Nüesch, and Arnold Barmettler
RADARSAT Terrain Geocoding and Radiometric Correction over Switzerland Geometric and Radiometric Calibration of RADARSAT Images David Small, Francesco Holecz, Erich Meier, Daniel Nüesch, and Arnold Barmettler
More informationSENTINEL-1 Toolbox. SAR Basics Tutorial Issued March 2015 Updated August Luis Veci
SENTINEL-1 Toolbox SAR Basics Tutorial Issued March 2015 Updated August 2016 Luis Veci Copyright 2015 Array Systems Computing Inc. http://www.array.ca/ http://step.esa.int SAR Basics Tutorial The goal
More informationRegion-based Segmentation and Object Detection
Region-based Segmentation and Object Detection Stephen Gould Tianshi Gao Daphne Koller Presented at NIPS 2009 Discussion and Slides by Eric Wang April 23, 2010 Outline Introduction Model Overview Model
More informationarxiv: v1 [cs.cv] 7 Sep 2013
Radar shadow detection in SAR images using DEM and projections arxiv:1309.1830v1 [cs.cv] 7 Sep 2013 V. B. S. Prasath O. Haddad Abstract Synthetic aperture radar (SAR) images are widely used in target recognition
More informationEE 701 ROBOT VISION. Segmentation
EE 701 ROBOT VISION Regions and Image Segmentation Histogram-based Segmentation Automatic Thresholding K-means Clustering Spatial Coherence Merging and Splitting Graph Theoretic Segmentation Region Growing
More informationIn addition, the image registration and geocoding functionality is also available as a separate GEO package.
GAMMA Software information: GAMMA Software supports the entire processing from SAR raw data to products such as digital elevation models, displacement maps and landuse maps. The software is grouped into
More informationCHAPTER 5 OBJECT ORIENTED IMAGE ANALYSIS
85 CHAPTER 5 OBJECT ORIENTED IMAGE ANALYSIS 5.1 GENERAL Urban feature mapping is one of the important component for the planning, managing and monitoring the rapid urbanized growth. The present conventional
More informationAN IMPROVED SAR RADIOMETRIC TERRAIN COR- RECTION METHOD AND ITS APPLICATION IN PO- LARIMETRIC SAR TERRAIN EFFECT REDUCTION
Progress In Electromagnetics Research B, Vol. 54, 107 128, 2013 AN IMPROVED SAR RADIOMETRIC TERRAIN COR- RECTION METHOD AND ITS APPLICATION IN PO- LARIMETRIC SAR TERRAIN EFFECT REDUCTION Peng Wang 1, 2,
More informationINCORPORATING A PRIORI KNOWLEDGE INTO A MOVING VEHICLE DETECTOR FOR TERRASAR-X DATA
INCORPORATING A PRIORI KNOWLEDGE INTO A MOVING VEHICLE DETECTOR FOR TERRASAR-X DATA A. Laika ), F. Meyer 2), S. Hinz ), R. Bamler,2) ) Remote Sensing Technology, Technische Universitaet Muenchen, Arcisstrasse
More informationLetter. Wide Band SAR Sub-Band Splitting and Inter-Band Coherence Measurements
International Journal of Remote Sensing Vol. 00, No. 00, DD Month 200x, 1 8 Letter Wide Band SAR Sub-Band Splitting and Inter-Band Coherence Measurements D. DERAUW, A. ORBAN and Ch. BARBIER Centre Spatial
More informationWIDE BASELINE INTERFEROMETRY WITH VERY LOW RESOLUTION SAR SYSTEMS
1 of 25 26/03/2008 22.35 ne previo WIDE BASELINE INTERFEROMETRY WITH VERY LOW RESOLUTION SAR SYSTEMS Abstract: A. Monti Guarnieri, C. Prati, F. Rocca and Y-L. Desnos (*) Dipartimento di Elettronica e Informazione
More informationGenerate Glacier Velocity Maps with the Sentinel-1 Toolbox
Making remote-sensing data accessible since 1991 Generate Glacier Velocity Maps with the Sentinel-1 Toolbox Adapted from the European Space Agency s STEP community platform In this document you will find:
More informationFIRST RESULTS OF THE ALOS PALSAR VERIFICATION PROCESSOR
FIRST RESULTS OF THE ALOS PALSAR VERIFICATION PROCESSOR P. Pasquali (1), A. Monti Guarnieri (2), D. D Aria (3), L. Costa (3), D. Small (4), M. Jehle (4) and B. Rosich (5) (1) sarmap s.a., Cascine di Barico,
More informationANALYSIS OF MULTIPATH PIXELS IN SAR IMAGES
ANALYSIS OF MULTIPATH PIXELS IN SAR IMAGES J. W. Zhao a,b, J. C.Wu a,*, X. L. Ding b, L. Zhang b, F. M.Hu a a College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai, China
More informationIN RECENT years, the frequency of natural disasters has
1658 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 6, JUNE 2007 A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage
More informationALOS PALSAR VERIFICATION PROCESSOR
ALOS PALSAR VERIFICATION PROCESSOR P. Pasquali (1), A. Monti Guarnieri (2), D. D Aria (3), L. Costa (3), D. Small (4), M. Jehle (4) and B. Rosich (5) (1) sarmap s.a., Cascine di Barico, 6989 Purasca, Switzerland,
More informationDEM-BASED SAR PIXEL AREA ESTIMATION FOR ENHANCED GEOCODING REFINEMENT AND RADIOMETRIC NORMALIZATION.
DEM-BASED SAR PIXEL AREA ESTIMATION FOR ENHANCED GEOCODING REFINEMENT AND RADIOMETRIC NORMALIZATION Othmar Frey (1), Maurizio Santoro (2), Charles L. Werner (2), and Urs Wegmuller (2) (1) Gamma Remote
More informationDIGITAL IMAGE ANALYSIS. Image Classification: Object-based Classification
DIGITAL IMAGE ANALYSIS Image Classification: Object-based Classification Image classification Quantitative analysis used to automate the identification of features Spectral pattern recognition Unsupervised
More informationSpectral Classification
Spectral Classification Spectral Classification Supervised versus Unsupervised Classification n Unsupervised Classes are determined by the computer. Also referred to as clustering n Supervised Classes
More informationEXPLOITATION OF DIGITAL SURFACE MODELS GENERATED FROM WORLDVIEW-2 DATA FOR SAR SIMULATION TECHNIQUES
EXPLOITATION OF DIGITAL SURFACE MODELS GENERATED FROM WORLDVIEW-2 DATA FOR SAR SIMULATION TECHNIQUES R. Ilehag a,, S. Auer b, P. d Angelo b a Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute
More informationDevelopment of building height data from high-resolution SAR imagery and building footprint
Safety, Reliability, Risk and Life-Cycle Performance of Structures & Infrastructures Deodatis, Ellingwood & Frangopol (Eds) 2013 Taylor & Francis Group, London, ISBN 978-1-138-00086-5 Development of building
More informationLand Cover Classification Techniques
Land Cover Classification Techniques supervised classification and random forests Developed by remote sensing specialists at the USFS Geospatial Technology and Applications Center (GTAC), located in Salt
More informationExploiting the High Dimensionality of Polarimetric Interferometric Synthetic Aperture Radar Observations
Exploiting the High Dimensionality of Polarimetric Interferometric Synthetic Aperture Radar Observations Robert Riley rriley@sandia.gov R. Derek West rdwest@sandia.gov SAND2017 11133 C This work was supported
More informationDigital Image Classification Geography 4354 Remote Sensing
Digital Image Classification Geography 4354 Remote Sensing Lab 11 Dr. James Campbell December 10, 2001 Group #4 Mark Dougherty Paul Bartholomew Akisha Williams Dave Trible Seth McCoy Table of Contents:
More informationFUSION OF OPTICAL AND INSAR FEATURES FOR BUILDING RECOGNITION IN URBAN AREAS
In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 --- Paris, France, 3-4 September, 2009 FUSION OF OPTICAL AND INSAR FEATURES FOR BUILDING RECOGNITION IN URBAN AREAS
More informationThe Earth-Observation Image Librarian (EOLib): The data mining component of the TerraSAR-X Payload Ground Segment
The Earth-Observation Image Librarian (EOLib): The data mining component of the TerraSAR-X Payload Ground Segment Daniela Espinoza Molina, Vlad Manilici, Octavian Dumitru, Christoph Reck, Shiyong Cui,
More information1. Make a top level directory structure for ascending or descending data as follows:
How to make an InSAR time series from Sentinel-1 TOPS data: Kilauea 1. Make a top level directory structure for ascending or descending data as follows: 2. Prepare a topography grid (dem.grd) a) Using
More informationThe 2017 InSAR package also provides support for the generation of interferograms for: PALSAR-2, TanDEM-X
Technical Specifications InSAR The Interferometric SAR (InSAR) package can be used to generate topographic products to characterize digital surface models (DSMs) or deformation products which identify
More informationStudy of the Effects of Target Geometry on Synthetic Aperture Radar Images using Simulation Studies
Study of the Effects of Target Geometry on Synthetic Aperture Radar Images using Simulation Studies K. Tummala a,*, A. K. Jha a, S. Kumar b a Geoinformatics Dept., Indian Institute of Remote Sensing, Dehradun,
More informationIMPROVING DEMS USING SAR INTERFEROMETRY. University of British Columbia. ABSTRACT
IMPROVING DEMS USING SAR INTERFEROMETRY Michael Seymour and Ian Cumming University of British Columbia 2356 Main Mall, Vancouver, B.C.,Canada V6T 1Z4 ph: +1-604-822-4988 fax: +1-604-822-5949 mseymour@mda.ca,
More informationAssessment of Polarimetric and Spatial Features for Built-up Mapping using ALOS PALSAR Polarimetric SAR Data
Assessment of Polarimetric and patial Features for Built-up Mapping using ALO PALAR Polarimetric AR Data hucheng YOU, China Key words: ALO PALAR, support vector machine, random forest, built-up mapping
More informationDERIVATION of the BACKSCATTERING COEFFICIENT σ o in ESA ERS SAR PRI PRODUCTS
ERS SAR CALIBRATION DERIVATION of the BACKSCATTERING COEFFICIENT σ o in ESA ERS SAR PRI PRODUCTS H. Laur 1, P. Bally 2, P. Meadows 3, J. Sanchez 4, B. Schaettler 5, E. Lopinto 6, D. Esteban 4 Document
More informationRADARGRAMMETRY AND INTERFEROMETRY SAR FOR DEM GENERATION
RADARGRAMMETRY AND INTERFEROMETRY SAR FOR DEM GENERATION Jung Hum Yu 1, Xiaojing Li, Linlin Ge, and Hsing-Chung Chang School of Surveying and Spatial Information Systems University of New South Wales,
More informationOCCLUSION BOUNDARIES ESTIMATION FROM A HIGH-RESOLUTION SAR IMAGE
OCCLUSION BOUNDARIES ESTIMATION FROM A HIGH-RESOLUTION SAR IMAGE Wenju He, Marc Jäger, and Olaf Hellwich Berlin University of Technology FR3-1, Franklinstr. 28, 10587 Berlin, Germany {wenjuhe, jaeger,
More informationSAR IMAGE PROCESSING FOR CROP MONITORING
SAR IMAGE PROCESSING FOR CROP MONITORING Anne Orban, Dominique Derauw, and Christian Barbier Centre Spatial de Liège Université de Liège cbarbier@ulg.ac.be Agriculture and Vegetation at a Local Scale Habay-La-Neuve,
More informationAirborne Differential SAR Interferometry: First Results at L-Band
1516 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 6, JUNE 2003 Airborne Differential SAR Interferometry: First Results at L-Band Andreas Reigber, Member, IEEE, and Rolf Scheiber Abstract
More informationDefining Remote Sensing
Defining Remote Sensing Remote Sensing is a technology for sampling electromagnetic radiation to acquire and interpret non-immediate geospatial data from which to extract information about features, objects,
More informationConcept and methodology of SAR Interferometry technique
Concept and methodology of SAR Interferometry technique March 2016 Differen;al SAR Interferometry Young s double slit experiment - Construc;ve interference (bright) - Destruc;ve interference (dark) http://media-2.web.britannica.com/eb-media/96/96596-004-1d8e9f0f.jpg
More informationInterferometric Synthetic-Aperture Radar (InSAR) Basics
Interferometric Synthetic-Aperture Radar (InSAR) Basics 1 Outline SAR limitations Interferometry SAR interferometry (InSAR) Single-pass InSAR Multipass InSAR InSAR geometry InSAR processing steps Phase
More informationIMPROVED TARGET DETECTION IN URBAN AREA USING COMBINED LIDAR AND APEX DATA
IMPROVED TARGET DETECTION IN URBAN AREA USING COMBINED LIDAR AND APEX DATA Michal Shimoni 1 and Koen Meuleman 2 1 Signal and Image Centre, Dept. of Electrical Engineering (SIC-RMA), Belgium; 2 Flemish
More informationGAMMA Interferometric Point Target Analysis Software (IPTA): Users Guide
GAMMA Interferometric Point Target Analysis Software (IPTA): Users Guide Contents User Handbook Introduction IPTA overview Input data Point list generation SLC point data Differential interferogram point
More informationSHIP DETECTION WITH SENTINEL-1 USING SNAP S-1 TOOLBOX - GULF OF TRIESTE, ITALY
TRAINING KIT - OCEA01 SHIP DETECTION WITH SENTINEL-1 USING SNAP S-1 TOOLBOX - GULF OF TRIESTE, ITALY Table of Contents 1 Introduction... 3 2 Training... 3 2.1 Data used... 3 2.2 Software in RUS environment...
More informationDETECTION AND RECONSTRUCTION OF RADAR FOOT PRINTS FOR MAP UPDATING
ISSN: 0976-3104 SPECIAL ISSUE: Computer Science ARTICLE DETECTION AND RECONSTRUCTION OF RADAR FOOT PRINTS FOR MAP UPDATING G.Vijayalakshmi 1, B. Sathyasri 2, V.Mahalakshmi 3 M. Anto Bennet 4* Electronics
More informationOIL SPILL MAPPING WITH SENTINEL-1 AUGUST 2017, KUWAIT
_p TRAINING KIT OCEA03 OIL SPILL MAPPING WITH SENTINEL-1 AUGUST 2017, KUWAIT Table of Contents 1 Introduction to RUS... 3 2 Oil spill mapping background... 3 3 Training... 3 3.1 Data used... 3 3.2 Software
More informationThe Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas
Article The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas Sandro Martinis 1, *, Simon Plank 1 and Kamila Ćwik 1,2 1 German Remote Sensing Data Center (DFD), German Aerospace
More informationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 3, MARCH /$ IEEE
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 3, MARCH 2010 1487 Building Height Retrieval From VHR SAR Imagery Based on an Iterative Simulation and Matching Technique Dominik Brunner,
More informationFLOOD MONITORING WITH SENTINEL-1 USING S-1 TOOLBOX - JANUARY 2015, MALAWI
TRAINING KIT HAZA01 FLOOD MONITORING WITH SENTINEL-1 USING S-1 TOOLBOX - JANUARY 2015, MALAWI Table of Contents 1 Introduction to RUS... 2 2 Training... 2 2.1 Data used... 2 2.2 Software in RUS environment...
More informationTerrain correction. Backward geocoding. Terrain correction and ortho-rectification. Why geometric terrain correction? Rüdiger Gens
Terrain correction and ortho-rectification Terrain correction Rüdiger Gens Why geometric terrain correction? Backward geocoding remove effects of side looking geometry of SAR images necessary step to allow
More informationA STATISTICAL-COST APPROACH TO UNWRAPPING THE PHASE OF INSAR TIME SERIES
A STATISTICAL-COST APPROACH TO UNWRAPPING THE PHASE OF INSAR TIME SERIES Andrew Hooper Delft Institute of Earth Observation and Space Systems, Delft University of Technology, Delft, Netherlands, Email:
More informationSAR interferometry applications for emergency mapping
SAR interferometry applications for emergency mapping D.Grandoni (e-geos S.p.A.) Copernicus EMS Annual User Workshop Ispra, June 21 st 2017 07 June 2016 e-geos2016 www.e-geos.it 1 Introduction: SAR Interferometric
More informationPolSARpro v4.03 Forest Applications
PolSARpro v4.03 Forest Applications Laurent Ferro-Famil Lecture on polarimetric SAR Theory and applications to agriculture & vegetation Thursday 19 April, morning Pol-InSAR Tutorial Forest Application
More informationMapping Regional Inundation with Spaceborne L-band SAR
Making remote-sensing data accessible since 1991 Mapping Regional Inundation with Spaceborne L-band SAR Using open-source software such as QGIS and GIMP Adapted from Bruce Chapman 1, Rick Guritz 2, and
More informationCROP MAPPING WITH SENTINEL-2 JULY 2017, SPAIN
_p TRAINING KIT LAND01 CROP MAPPING WITH SENTINEL-2 JULY 2017, SPAIN Table of Contents 1 Introduction to RUS... 3 2 Crop mapping background... 3 3 Training... 3 3.1 Data used... 3 3.2 Software in RUS environment...
More informationIncidence Angle Normalization of Backscatter Data. I. Mladenova and T. J. Jackson Feb. 25, 2011
Incidence Angle Normalization of Backscatter Data I. Mladenova and T. J. Jackson Feb. 25, 2011 Introduction SMAP: Soil Moisture Active Passive (NASA/JPL, 2014) We need backscatter data for algorithm development
More informationChange Detection and Classification Using High
Provisional chapter Chapter 5 Change Detection and Classification Using High Change Detection and Classification Using High Resolution SAR Interferometry Resolution SAR Interferometry Azzedine Bouaraba,
More information