SARscape. Table of Contents. Preface 1. Overview 3. Basic Module 5. Focusing Module 11. Gamma and Gaussian Filtering Module 11

Transcription

1 Table of Contents Preface 1 Overview 3 Basic Module 5 Focusing Module 11 Gamma and Gaussian Filtering Module 11 Interferometry Module 13 ScanSAR Interferometry Module 18 Polarimetry and Polarimetric Interferometry Module 19I Interferometry Stacking Module 22 Quality Assessment Tool 24 Key Features 28

2 Preface SARscape was developed by: sarmap Founded in 1998 as a spin-off of the Remote Sensing Laboratories at the University of Zurich, sarmap provides algorithms, software, application development, and consulting, to the remote sensing field with focusing on air and space borne SAR data processing. For more information visit Aresys Established in 2003, by a group of professors and engineers from the Politecnico di Milano, the organization specializes in digital signal processing applied to remote sensing, more information is available at Privateers Privateers was founded in 1996 by a group of leading scientists in optical and radar remote sensing, aerospace engineering, and agronomy. Applications are focused on marine environment, hydro-geological survey, forest inventory, agriculture monitoring, and space reconnaissance. For more information visit Synthetic Aperture Radar (SAR) systems data acquisition: Single or dual channel mode (HH, HH/HV, or VV/VH) Interferometric (single- or repeat-pass) mode Polarimetric mode (HH, HV, VH, VV) Combination of interferometric and polarimetric acquisition modes SAR systems processing techniques: - Processing of SAR Intensity generation is limited to the intensity processing - Interferometric SAR (InSAR/DInSAR) Processing generation includes intensity, and interferometric phase processing - Polarimetric SAR (PolSAR) Processing generation includes intensity, and polarimetric phase processing - Polarimetric-Interferometric SAR (PolInSAR) Processing includes intensity, polarimetric, and interferometric phase processing pg. 1 of 29

3 SARscape processing techniques are supported in the following modules: Basic Module A set of processing steps for the generation of SAR products base on intensity which include a multi-purpose tool, and are complemented by: Focusing Module supports RADARSAT-1, ENVISAT ASAR, and ALOS PALSAR data. Gamma and Gaussian Filtering Module SAR specific filters reduce speckle while preserving the radar reflectivity, the textural properties, and the spatial resolution, especially in strongly textures SAR images. Interferometry Module Processing Interferometric SAR (2-pass interferometry, InSAR) for the creation of Digital Elevation Model and Coherence maps, as well as, the processing of Differential Interferometric SAR data (n-pass interferometry, DInSAR) for the creation of Land Displacement/Deformation maps, complemented by: ScanSAR Interferometry process InSAR and DInSAR data over large areas (400 by 400 km) SAR Polarimetry and Polarimetric Interferometry process polarimetric and polarimetric interferometric SAR data Persistent Scatters determine mm-scale displacements of individual features on the ground Quality Assessment Tool a set of functions for the SAR data quality assessment in geometric, radiometric, and polarimetric terms. pg. 2 of 29

4 Overview SARscape is a modular set of functions dedicated to the generation of products based on the following space-borne Synthetic Aperture Radar (SAR) sensors: ERS-1 and 2 SAR JERS-1 SAR RADARSAT-1 and 2 ENVISAT ASAR ALOS PALSAR TerraSAR X-1 COSMO SkyMed-1-2 (3-4 after launch) RISAT-1 (after launch) The following airborne SAR systems: OrbiSAR-1 (X- and P-band) E-SAR SARscape consists of eight complementary modules: Basic Module The Basic Module includes a set of processing steps (e.g. focusing, multi-looking, co-registration, de-speckling, feature extraction, coherence, geocoding, radiometric calibration and normalization, segmentation, and mosaicing) for the generation of airborne and space-borne SAR products. Based on intensity and coherence this module is complemented by a multi-purpose tool, and includes image visualization to Digital Elevation Model import and interpolation, to cartographic and geodetic transforms. Focusing Module The Focusing Module (developed by Aresys) extends focusing capabilities to RADAR- SAT-1 (Fine Beam and Standard Beam), ENVISAT ASAR (Alternating Polarization, Image, and Wide Swath), and ALOS PALSAR (Fine, Dual Pol, Full Pol, ScanSAR) data. In all cases an optimized phase preserving ω-κ processor is applied. Gamma and Gaussian Filter Module The Gamma and Gaussian Filter Module is an extension of the Basic Module, it includes SAR specific filters and algorithms (developed by Privateers), which are based on Gamma/ Gaussian-distributed scene models. This module is particularly efficient in reducing speckle, while preserving the radar reflectivity, the textural properties, and the spatial resolution, especially in strongly textured SAR images. pg. 3 of 29

5 Interferometry (InSAR and DInSAR) Module The Interferometry Module supports the processing of Interferometric SAR (2-pass interferometry and InSAR) for the generation of Digital Elevation Model and Coherence maps, as well as, the processing of Differential Interferometric SAR data (n-pass interferometry and DInSAR) for the generation of Land Displacement/Deformation maps. ScanSAR Interferometry (InSAR and DInSAR) Module The ScanSAR Interferometry Module was developed by Aresys and based on an algorithm of Politecnico di Bari (POLIBA); it extends the Interferometry Module to the ScanSAR mode and is capable of generating interferograms over large areas (400 x 400 km). This module also enables the generation of Digital Elevation Model, Coherence, and Land Displacement maps. Polarimetry & Polarimetric Interferometry (PolSAR and PolInSAR) Module The PolSAR and PolInSAR Module supports the processing of polarimetric and polarimetric interferometric SAR data. Interferometry Stacking Module The Interferometry Stacking Modules, developed in collaboration with Aresys, is able to determine mm-scale displacements of individual features on the ground Quality Assessment Tool (QAT) Module The Quality Assessment Tool Module provides qualitative and quantitative information about the quality of SAR data in geometric, radiometric, and polarimetric terms. Note that the Basic Module, Interferometry Module, and Quality Assessment Tool Module are standalone modules, the other modules require the Base Module or the Interferometry, refer to the table below. SARscape modules Basic Interferometry Focusing * Gamma and Gaussian Filtering ScanSAR Interferometry PolSAR and PolInSAR Persistent Scatterers * The Focusing Module requires the Basic Module or the Interferometry Module. pg. 4 of 29

6 SARscape requires a minimum of 512MB RAM, 18GB HD and a 500MHz processor, and it is interfaced to ENVI (ITT Visual Information Solutions) enabling the simple launch of the processing factions via standard dialog panels. This calls the data processing routines which are written in C++. The figure below shows how SARscape appears in the ENVI software environment. SARscape supports Windows 2000/XP/Vista and Linux 2.6 operating systems. Basic Module The Basic Module provides a set of processing steps for the generation of space-borne and airborne SAR products based on intensity and coherence. The processing is complemented by a multi-purpose tool, which includes a wide range of functions, from image visualization, to Digital Elevation Module import and interpolation, to cartographic and geodetic transformations. The Basic Module also supports the following processing capabilities: Focusing and Multi-looking Support Module The SAR Focusing Module generates ERS-1/2 and JERS-1 SAR data complex images from amplitude and phase information, and is based on the ω-κ frequency domain algorithms. This Module can also generate a multi-look image from a Single Look Complex (SLC) image by averaging the power across a number of lines in both the azimuth and range directions. pg. 5 of 29

7 Co-registration Support Module When multiple image data sets cover the same region, it is necessary for the pixels from different images to correspond in order to ensure pixel-by-pixel comparisons can be effectively carried out. In cases where pixel sizes vary, spatial registration and resampling may be need. An automatically occurring co-registration is completed based on maximizing correlation in a number of windows. Filtering Support Module This module includes multiple conventional single-image filters based on the classical minimum mean square error and on the anisotropic non-linear diffusion theory. The filters are complemented by two advanced multi-temporal, multi-sensor, de-speckling filters. Feature Extraction Support Module Feature parameters, generally used as inputs for classification or quantitative analysis, can be extracted from intensity and coherence; these processes are based on first order and time-series statistics. Geocoding, Radiometric Calibration, and Normalization of SAR data Support Module Using nominal parameters or Ground Control Points (GCP), ellipsoidal or terrain geocoding allows for the transformation from radar coordinates (grand or slant range) into a given cartographic reference system. Following identification of GCPs within in a SAR image, the GCPs are automatically archived. The geocoding in this support module is based on a rigorous range-doppler approach and the radiometric calibration (performed during geocoding) is based on the exploitation of the radar equation. Finally, this support module enables for the empirical radiometric normalization, correcting the effects of incidence angle or sigma nought by the application of a modified cosine correction. Mosaicing Support Module Areas of interests are often covered by several images, SAR images (amplitude and/or coherence) and products (classification results) can be combined, with the Mosaicing Support Module, to obtain complete coverage of an area. Seamlessly joining image frames together, this support module creates a consistent mosaic across a region of interest by refining the radiometric variations in images. pg. 6 of 29

8 Segmentation Support Module This support module performs segmentation of SAR/InSAR and/or optical multi-spectral data in four steps. First, filtering the data by means of a single or a multi-temporal anisotropic diffusion; second, applying an extended Canny edge detector to single or multi-temporal data; third, performing a single- or multi-temporal region growing; finally, applying a time-varying segmentation aiming at the detection of temporal changes. Tools Support Module SARscape implements a wide range of tools that perform the following functions: GTOPO30, ACE and SRTM Digital Elevation Model data reading and automatic mosaicing Multi-source Digital Elevation Model combination Shape, image and digital Elevation Model cartographic and geodetic transformation Slope image generation Point target analysis (SAR data) Image statistics calculation Multi-image operations Specific indexes (e.g. NDVI) generation Image re-sampling Images and DEMs interpolation Sample selection Pan sharpening Raster to shape data conversion Raster data slicing Complex interferogram to phase and module conversion and backward transformation Cartographic to slant geometry conversion File transformations pg. 7 of 29

9 A Note on SAR (Intensity) Data Processing There is no standard processing chain; the processing depends upon how the SAR data have been acquired. The type of product also determines how SAR products will be processed. Depending on the acquisition process of SAR data, one of the following three processing procedures can be used: Single-sensor, Single-mode, Multi-temporal Approach Used for multi-temporal SAR data that are acquired in the same mode (e.g. ENVISAT ASAR Image Mode 4), the geometry remains the same and the following steps may be used: Focusing Multi-looking Co-registration Multi-temporal speckle filtering (De Grandi) Terrain geocoding, radiometric calibration and normalization Single-sensor, Multi-mode, Multi-temporal Approach Use in single-sensor data, SAR data that are acquired in different geometries (and types of acquisition) are not linked to standard repeat pass cycle geometry (ESR-1/2), the following steps may be used: Focusing Generation of 1-look Intensity Terrain geocoding, radiometric calibration and normalization Multi-temporal speckle filtering Multi-sensor, Multi-mode, Multi-temporal Approach The most advanced data processing, based on the principle of a satellite s constellation, SAR data are acquired in different geometries by different sensors. The data acquisitions can be optimized in terms of temporal, and spatial and spectral resolution. The processing chain corresponds to the previous processing chain making it imperative that the data are accurately absolutely radiometrically calibrated. pg. 8 of 29

10 Examples Single Data mosaic based on 1250 JERS-1 SAR images of Central Africa. Multi-temporal mosaic based on 180 ENVISAT ASAR images of Senegal. pg. 9 of 29

11 Map of the rice emergence during the crop season 2003/4 derived from ENVISAT ASAR and RADAR- SAT-1 combined acquisition (Luzon Philippines) Map of burnt areas based on multi-temporal ERS-2 SAR data (Canada). The agreement between the EO product and the burnt areas detected by the Canadian Forest Service, different colors correspond to the plant re-growth. pg. 10 of 29

12 Focusing Module The Focusing Module was developed by Aresys and extends the focusing capabilities of SARscape to RADARSAT-1, ENVISAT ASAR, and ALOS PALSAR data. Based on the ω-κ focusing kernel, the Focusing Module handles mono as well as bistatic SAR, ScanSAR, Spotlight, among others. A result of the combination of geophysics and SAR, the ω-κ is well suited to provided fast and accurate focusing for any squint angle (0 to 89 ) and wide apertures, and was designed with an emphasis on geometric accuracy (localization) for SAR Interferometry single and multi mode. The Focusing Module is profiled and optimized using Intel Math Kernel Libraries, taking full advantage of the multithreading and streaming SIMD extensions of the latest INTEL family processors, BLAS and LAPACK linear algebra libraries, and AMD and Intel architecture optimized FFT routines. This Module is able to provide Single Look Complex (SLC) and detect products starting from the following raw data: ENVISAT Image Mode data ENVISAT Alternating Polarization data ENVISAT Wide Swath data RADARSAT-1 Fine Beam mode RADARSAT-1 Standard Beam mode ALOS PALSAR Single Polarization ALOS PALSAR Dual Polarization ALOS PALSAR Full Polarization Single Look Complex data generated with this Module are not appropriate to derive absolutely radiometric calibrated values. Gamma and Gaussian Filtering Module The Gamma and Gaussian Filtering Module is dedicated to the speckle removal in SAR images and includes a variety of SAR specific filters. The algorithms, developed by Privateers, based on Gamma/Gaussian-distributed scene models reduce speckle noise, while preserving the radar reflectivity, the textural properties, and the spatial resolution, especially in strongly texture SAR images. The Gamma and Gaussian Filtering Module provides specific algorithms that can be applied to: pg. 11 of 29

13 - Single- or multi-look, single channel detected products - Single- or multi-multichannel detected products - Single Look Complex (SLC) SAR images - Separate SLC channels - Full polarimetric SAR data In the presence of scene texture and to preserve useful spatial resolution, an A Priori first order statistical module is needed, this can restore the spatial fluctuations of the radar reflectivity. The Gamma-distributed scene model, widely considered to be the most effective for SAR clutter, modulated by either an independent complex-gaussian speckle model (for SAR SLC images) or by a Gamma speckle model (for multi-look detected SAR images) gives rise to a K-distributed clutter. The Gaussian-distributed scene model for mathematical tractability of the inverse problem in case of multi-channel SAR images multivariate A Priori scene distributions. The supported SAR filtering methods are: Detected SAR data Single channel detected SAR images Gamma-Gamma MAP (Maximum A Posteriori) filter Gamma-DE (Distribution-Entropy) MAP filter Gamma APM (A Posteriori Mean) filter Multichannel detected SAR images Gamma-Gamma MAP filter for uncorrelated speckle Gaussian-Gaussian MAP filter for correlated speckle Gaussian-DE MAP filter for correlated speckle Complex SAR data Single Look Complex (SLC): Complex Gaussian-DE MAP filter Separate complex looks or interferometric series: Complex Gaussian-Gamma MAP filter Polarimetric SAR data Complex Wishart-Gamma MAP for polarimetric SAR data Complex Wishart-DE MAP for polarimetric SAR data pg. 12 of 29

14 These figures show examples of ERS-1 PRI (upper left) and it s filtered version (upper right), RADARSAT-1 SGF (lower left) and its filtered version (lower right). The images were filtered with the Gaussian-Gaussian MAP filter for multi-channel detected multilook SAR images. Interferometry Module The Interferometry Module supports the processing of Interferometric SAR (2-pass Interferometry, InSAR) and Differential Interferometric SAR (n-pass Interferometry, DIn- SAR) data for the generation of Digital Elevation Model, Coherence, and Land Displacement maps. The processing includes the following steps: pg. 13 of 29

15 - Phase-Preserving SAR Focusing A ω-κ frequency domain algorithm implemented to guarantee the lowest phase distortion and to preserve the original interferometric coherence. - Coarse and Fine Co-registration Using Digital Elevation Model to provide the proper parameters in order to align the same points in two images. - Interferogram Generation Spectral shift filtering performed on an image air and the Hermitian Product is calculated. - DEM Flattening Synthetic fringes generated from a coarser resolution DEM or ellipsoidal height, using a backward geocoding approach, and then cross-multiplied by the SAR interferogram. The step allows for the removal of most low frequency components of the wrapped phase, to ease the following phase unwrapping. - Adaptive Filtering The flattened complex interferogram is filtered to improve the phase SNR, improving the height accuracy of the final DEM. - Phase Unwrapping The filtered interferogram is unwrapped by using a region growing approach. - Phase Edit Unwrapping errors can be corrected in a semi-automatic or completely manual way. - Geometry Optimization, base on Ground Control Points The orbits are refined in order to obtain an accurate conversion of the phase information to height and proper geo-location. Both manual and automatic procedures for the identification of the most suitable image pixels to be used as GCPs are implemented. - Synthetic Phase Added back to the unwrapped phase, ensures that the final DEM is derived only from the SAR data. - Phase to Map Conversion A rigorous approach is implemented, that takes into account the range-doppler nature of the SAR acquisition. The final product is obtained in the desired cartographic reference system by taking into account all the necessary geodetic and cartographic parameters and transformations. - Mosaicing The area not covered is minimized by combining DEM obtained from different acquisition orbits. pg. 14 of 29

16 - Phase to Displacement The phase values are converted to displacement and geocoded onto a map projection. The user can enter a specific direction (azimuth angle) and inclination angle along which the main deformation is assumed, or even enter an a-priori known deformation field as input. For the generation of displacement maps it is mandatory to run a second iteration of the processing, from the interferogram flattening to the phase unwrapping, after the execution of the baseline fit step (which is aimed at correcting orbital inaccuracies). 2-Pass SAR Interferometry Examples Figure 1 shows the Digital Elevation Model of Switzerland, where height values are ranging from 195 m (darkest grey tones) to 4634 m (lightest grey tones). The horizontal resolution of this product is 25 meters, while the height accuracy varies from 7 (moderate topography) to 15 meters (steep topography). Digital Elevation Map of Switzerland pg. 15 of 29

17 Differential SAR Interferometry (n-pass Interferometry) An example of differential interferometry showing differential interferogram, coherence and displacement map illustrated in Figure A. The images are relevant to the analysis of the earthquake event on December 26, 2003 in Bam, Iran. Figure A shows the differential interferogram generated from the ENVISAT ASAR pair acquired on December 3, 2003 (pre-earthquake) and February 11, 2004 (postearthquake); a reference InSAR DEM was used for subtracting the topography induced fringes. Figure A Differential Interferogram (Bam, Iran). pg. 16 of 29

18 Figure B shows the coherence image relevant to the December 3, 2003 and February 11, 2004 ENVISAT ASAR pair. It is important to note that lowest coherence values (darkest tones) correspond both to steep slopes or vegetated areas (especially visible in the lower part of the image) and to the Bam built zone (image center), which was completely destroyed by the earthquake. Figure B Coherence (Bam, Iran) Figure C shows the deformation map obtained considering a strike slip fault (horizontal movement) with a N_S oriented fault plane. The red and green areas represent the largest zones of deformation (in opposite direction), while yellow areas correspond to smaller deformation zones. Figure C Displacement Map (Bam, Iran) pg. 17 of 29

19 ScanSAR Interferometry Module The ScanSAR Interferometry Module, developed by Aresys, is based on an original algorithm from Politecnico di Bari (POLIBA) and extends the SARscape Interferometry Module to the ScanSAR mode, providing the capability to generate interferograms over large areas (400 x 400 m). This module enables the user to generate Digital Elevation Model, Coherence and Land Displacement maps based on ENVISAT ASAR Wide Swath data. By combining StripMode (ASAR Image Mode) data with ScanSAR (ASAR Wide Swath) data, this module produces hybrid interferometric products. Featured capabilities of the ScanSAR Interferometry Module: - ScanSAR Single Look Complex (SLC) Pair Overlap Estimation: ScanSAR data alignment and burst overlapping are estimated using sensor orbit descriptions. - Doppler Common Band Filtering: Common azimuth bandwidth is selected on the master and slave bursts, minimizing the single de-correlation. - Burst Co-registration: Co-registration coefficients are computed using sensor orbit descriptions, the slave burst is then re-sampled over the master burst grid. - Interferogram Generation: Co-registration bursts are filtered along the range direction in order to minimize the geometrical de-correlation; either Synthetic fringes or a flat Earth interferogram can be used. The interferogram is subsequently generated as a Hermitian Product between master and co-registered slave bursts. - Single Swath Interferogram and Single Swath Detected Image Generation: Single burst interferograms are collected and coherently summed providing a multilooked interferogram image for each ScanSAR swath. In the same step, the single burst absolute value data are collected, weighted with a de-scalloping window, and incoherently summed, providing two (master and slave) single swath, co-registered detected images. - Swaths Recomposing: Single swath data (interferogram and detected data) are composed into multiswath images. pg. 18 of 29

20 Examples in the Figure below show a differential interferogram (left) and the corresponding coherence (right) generated from an ENVISAT ASAR Wide Swath pair (400 km x 400 km) acquired in the area of Bam, Iran. The data was collected on September 2, 2003 (pre-earthquake) and on June 8, 2004 (post-earthquake). Compare this differential interferogram with the interferogram in Figure 2a. The differential interferogram (left) and corresponding coherence (right) were generated from an ENVISAT ASAR Wide Swath pair (400 x 400 km). Polarimetry and Polarimetric Interferometry Module The Polarimetry and Polarimetric Interferometry Module supports the processing of polarimetric and polarimetric interferometric SAR data, the processing capabilities include: Polarimetric Calibration Matrix Polarimetric Calibration allows users to minimize the impact of non-ideal behaviors of a full polarimetric SAR acquisition system, in order to obtain the most accurate imaged as possible, from the available measurements, estimates of the scatting matrix are made. The polarimetric calibration is performed based on defined calibration matrixes. Polarimetric Signature This function provides an estimate of the polarimetric signature of a point-target-like object, whose location is, specified either in terms of its range and azimuth indexes within a slant-range acquisition or through its known geographic coordinates. pg. 19 of 29

21 Polarimetric Synthesis The availability of a full polarimetric acquisition, for example based on a linearly polarized base, allows synthesizing any desired elliptical polarization. Starting from a set of full polarimetric linearly polarized Single Look Complex (SLC) data, this module allows the user to synthesize a new set of SLC data in a desired orthogonal basis, either circular, linear (rotated 45 ), or generic elliptical of user defined orientation and ellipticity. Polarimetric Features This function perform the computation of conventional polarimetric features, including: the Linear Depolarization Ratio, the Polarimetric Phase Difference, and the Polarization HH-VV Ratio, all based on the scattering matrix. Pauli Decomposition The Pauli coherent decomposition provides an interpretation of the full polarimetric SLC data set in terms of elementary scattering mechanisms: sphere/plate/trihedral (single or odd-bounce scattering), dihedral oriented at 0 (double or even-bounce), and deplane oriented at 45 (qualitatively related to volume scattering). Pauli Decomposition based on ALOS PALSAR PLR data (data copyrighted by JAXA). Entropy-Anisotropy-Alpha Decomposition The Entropy-Anisotropy-Alpha Decomposition function performs an eigen-decomposition of the coherency matrix of a full polarimetric SLC data set. To facilitate the analysis of the physical information provided by the eigen-decomposition, three secondary parameters are defined as a function of eigen-values and eigen-vectors. These parameters are: pg. 20 of 29

22 Entropy relates to the degree of randomness of the scattering process Anisotropy measures the relative importance of the second and third eigen-value of the eigen-decomposition Alpha relates to the type of scattering mechanism Entropy-Anisotropy-Alpha Decomposition based on ALOS PALSAR PLR data (Data copy write of JAXA). Entropy-Anisotropy-Alpha Classification The Entropy-Anisotropy-Alpha Classification function performs an unsupervised classification of a previous Entropy-Anisotropy-Alpha Decomposition step results, identifying the main scattering type of every pixel in the target area. The main processing capabilities for Polarimetric SAR Interferometry are: Single Look Complex (SLC) Co-registration Performs an unsupervised classification of the results of a previous Entropy-Anisotropy- Alpha Decomposition step, identifying the main scattering type of every pixel in the target area. Polarimetric Phase Difference (PPD) / Interferogram Generation Performs the Polarimetric Phase Difference using the HH and VV polarization of the same acquisition, and it generates interferograms using the same polarization of the PolInSAR pair. Polarimetric / Interferometric Coherence Generates correlation/coherence images based on Polarimetric Phase Difference / Interferogram products. Coherence Optimization Estimates the main scattering mechanisms of a full polarimetric pair of linear acquisitions, identifying those mechanisms that correspond to the highest value of interferometric coherence. The corresponding interferograms and coherence maps are provided as a result. pg. 21 of 29

23 Interferometry Stacking Module The Interferometry Stacking Module, developed by sarmap and Aresys, enables the users to measure very small ground displacements and to trace the deformation rate related to natural or man made phenomena (e.g. volcanic or seismic activity, landslides, subsidence, building collapses, etc.), with excellent accuracy and spatial and temporal detail. This technique, extending SAR interferometry to the analysis of large multi-temporal observations, enables the user to improve the measurement accuracy from a few centimeters (classical interferometry approach) to a few millimeters (Persistent Scatterers approach). Additionally, limitations typical of SAR interferometry (e.g. atmospheric distortions or temporal de-correlation) are significantly reduced. This method relies on the identification of a certain number of coherent radar signal reflectors (Persistent Scatterers, PS), focusing the process on the analysis of the phase history of the single points (pixels), as opposed to the conventional interferometric approach. The end result consists of the displacement history of every single PS together with its average displacement rate. Examples of PS candidates are typically represented by urban features (e.g. house roofs, bridges, antennas, etc.), other manmade structures (e.g. green houses, dams, metallic and concrete features), and exposed rock outcrop formations. This process requires that data have a minimum density of the scatterers (5-10 per square km) must be detectable in the SAR viewing geometry, and a large number of SAR observations (15 to 20 images) obtained from the same sensor with the same acquisition geometry. The temporal distribution of the acquisition should also be adequate to compare with the expected dynamics of the displacements under analysis. The Interferometry Stacking Module supports SAR data acquired by ERS and ENVISAT satellites and is a stand-alone module. However, in order to exploit the complete potential of this module, its combined use with the Basic Module and the Interferometry Module are advised. The SARscape integrated module approach will enable the user to comprehensively process SAR multi-temporal series in terms of both intensity (e.g. accurately filtered, geocoded, and calibrated images generated by the Basic Module) and interferometric products (e.g. coherence images, interferograms, DEMs, and Displacement maps generated by the Interferometry Module). pg. 22 of 29

24 Standard Persistent Scatterers outcomes: map (top) and Persistent Scatterers history (bottom). pg. 23 of 29

25 Quality Assessment Tool The correct use of Earth Observation data implies a rigorous data calibration in geometric, radiometric, polarimetric, and interferometric terms. SAR systems relying on polarimetric and interferometric data, with the primary goals of: Analyze multi-temporal images acquired from the same sensor Analyze images acquired from different sensors Generate indexes Infer bio-physical or geo-physical parameters Despite the large effort over the past two decades by calibration/validation working groups, the quality of pre-processed images (generation of co-registered and/or orthorectified radiometrically calibrated products) still remain questionable, the main reasons being: Inappropriate models (often limited to empirical models) Limitations of software tools Inappropriate input images Inappropriate sensor parameters Inaccurate platform parameters Inaccurate Ground Control Points (GCP) or Tie Points (TP) Inaccurate Digital Elevation Model (DEM) In order to quantitatively assess the geometric, radiometric, and polarimetric quality of SAR products, a customized Quality Assessment Tool (QAT) based on solid methodology is necessary. The overall architecture of the Quality Assessment Tool system, which supports the quality assessment for ENVISAT ASAR (IM/AP/WS), RADARSAT-1 and 2 (FB, SB, WB, EB, ScanSAR), is illustrated in the following figures. The main packages include: The implementation of the Human Machine Interface The ingestion of different products and of execution of all supported tests A database package which stores all configuration settings, references and nominal parameters, and GCP measurements, as well as, the results of the analysis of different SAR products A database connection layer responsible for interfacing the results of different processing modules with a database pg. 24 of 29

26 Overall architecture of the QAT package The following quality parameters and assessment procedures are supported: Background removal and image interpolation Impulse Reponses Function measurements Spatial resolution in range and azimuth Peak Side Lobe Ratio Range and azimuth Peak Side Lobe Ratio Spurious Side Lobe Ratio Integrated Side Lobe Ratio Derivation of sigma nought and radiometric calibration Sigma nought derivation for ASAR ground range projected products Sigma nought derivation for ASAR single complex slant range projected products Beta nought for derivation or RADARSAT-1 detected products Beta nought for derivation for RADARSAT-1 single look complex products Conversion from beta bought to sigma nought pg. 25 of 29

27 Calibration constant determination Radiometric analysis Radiometric accuracy Radiometric resolution Equivalent Number of Looks Radiometric stability Noise equivalent sigma nought Ambiguity analysis Azimuth ambiguity location offset Range ambiguity location offset Point target ambiguity offset Geometric analysis Level 1 products geocoding Absolute location accuracy Sampling Window Start Time bias Ground Control Point geocoding and residual analysis Swath Width and position Channel co-registration Multi-temporal co-registration Ratio Polarimetric analysis Polarimetric Phase Difference and Polarimetric Coherence Polarimetric signature The Quality Assessment Tool is comprised of three parts: The Graphical User Interface (GUI), data, and information display are all integration within the ENVI environment (for both Windows and Linux ). This allows the user to obtain professional tools for data visualization of both raster and vector data in standard formats and enables the user to exploit the existing basic analysis tools included in ENVI. This drives the execution of different Quality Assessment Modules in an interactive way, and displays the results through images, textual data, and plots. All modules related to initial data import and assessment are available as executable modules, originally written in C++ and may be implemented through the GUI and as command-line modules. Data import of SAR data originating from different platforms can be stored and delivered in different formats, through the interfacing of the QAT system. During the import phase, stored data are analyzed and all necessary parameters are extracted and stored in a XML file with a common syntax (included in the delivered documentation). The binary data (image products) are stored in a plain matrix (BIL) file and a corresponding.hdr file is generated, adhering to pg. 26 of 29

28 ENVI specifications, which allows for the direct display of the binary data within the ENVI environment. All Quality Assessment Modules import data directly into a common meta-format. All results of the Quality Assessment Modules are intercepted and stored within a history database for reference and nominal purposes. Part of the database is dedicated to storing information concerning Ground Control Points, which can be reused during long running assessments of SAR data quality over the same reference test-site. Quality Assessment Tool Graphical User Interface pg. 27 of 29

29 Key Features Among the several functionalities included in the software package, these features distinguish SARscape : Phase-preserving SAR Focusing high precision SLC data can be obtained from SAR raw data, starting from either Image mode or ScanSAR products, using the ω-κ algorithm Automatic co-registration of multi-temporal SAR or optical image stacks using cross-correlation techniques, pixel-accuracy is obtained without need of manual selection of corresponding points. Multi-temporal speckle filter when several SAR acquisitions are available over the same area, this additional unique filtering functionality allows users to obtain high speckle reduction while conserving all spatial and temporal information. A dedicated SAR filtering module based on the most advance Gamma and Gaussian distributed scene models; this module is suitable for SLC, PRI, multi-temporal data sets, etc. One-step coherence map computations all processing necessary steps to generate such products can be performed as one operation. Rigorous geocoding of SAR data (from slant range or ground range geometry) based on Range-Doppler equations, the accurate terrain correction can be per formed when a DEM is available; radiometric correction is performed by accounting for all instruments (e.g. antenna pattern and range spread loss) and terrain influences. Precise geocoding using nominal parameters, without Ground Control Points (GCPs) may be performed when accurate orbital information is available (e.g. ERS and ASAR). For ENVISAT ASAR data, DORIS orbits can be inputted to get geocoding sub-pixel accuracy using nominal parameters (e.g. no GCPs). If orbital information must be refined, the number of GCPs necessary can be reduced to one and can still obtain accurate geocoding. The identification of Ground Control Points in the SAR image supported by archiving. Radiometric and geometric calibration of optical data can be performed during geocoding based on DEM information, when available. A cartographic reference systems is available for the geocoded products, users can extend the reference system by providing all necessary parameters in the ASCII files. Batch processing long sequences of processing steps can be collected in one batch command and run as a single operation, script programming is also available. The InSAR and DInSAR processing chain can be applied to both medium resolution (e.g. ASAR Wide Swath products) and high resolution (e.g. ASAR Image Mode products) data. pg. 28 of 29

30 Tailored and dedicated data mosaicing routines are developed to unite at the best amplitude and DEM images. High accuracy DEMs can be generated using the Interferometric Module. Differential interferograms can be generated using the Interferometric Module to estimate small terrain movements, up to a few millimeters). Import of external DEMs; USGS GTOPO-30 and SRTM-3 world-wide DEMs can be automatically extracted and mosaiced over an area of interest by imputing the reference slant range or geocoded image. Support of Windows and Linux operating systems. Integration with the powerful image processing environment ENVI, allowing users to exploit a wide base of tools together with the specific capabilities of SARscape. Users can select the environment best suited for the applications in use, accessing the same processing routines and sharing data between the two platforms. Standard open formats used for all generated products. pg. 29 of 29