ENVI SARscape® allows you to easily read, process, and output your SAR data so you can transform hard-to-interpret data into meaningful, contextual information. Synthetic Aperture Radar (SAR) data is a sophisticated data type that can be used to analyse an area of interest during the day or night, regardless of weather conditions. It is particularly helpful when trying to detect change and study the topography of a specific area.
The newest release of SARscape – version 5.1 – makes it even easier to extract accurate and advanced information from SAR data. The following topics are of major interest:
Updated Raster and Vector Time Series analysis tools allow extended editing of plots and provide direct access to the Time Series statistic attributes.
GPU-accelerated geocoding has been introduced, based on a new algorithm. This can exploit the computational efficiency of modern platforms.
The following workflows have been introduced:
A new set of functions has been developed for Stereo SAR / Radargrammetric DEM generation starting from pairs of very high resolution slant range or ground range products acquired with significantly different acquisition angles. These DEMs can complement the ones obtained from InSAR processing. The DEM fusion routines are well suited to merge the products obtained with the different approaches. The chain has been successfully tested and validated with COSMO-Skymed, Radarsat-2, and TerraSAR-X pairs.
GPU-accelerated synthetic interferogram generation and flattening is now available, based on a new, more accurate algorithm and exploiting the computational efficiency of modern platforms, when available.
The following workflows have been introduced:
ENVI SARscape includes a set of processing tools to help you transform raw SAR data into an easy-to-interpret image for further analysis. ENVI SARscape processing tools are designed to work with a single image or a time series of images. This puts the data in an image format that allows you to derive useful information from it, such as extracting features of interest like buildings, or examining crop growth over a growing season.
ENVI SARscape filters reduce speckle, while preserving radar reflectivity, textural properties and spatial resolution, especially in strongly textured SAR images. These filters are particularly effective with polarimetric data. Additional SAR specific filters have algorithms based on Gamma/Gaussian-distributed scene models.
Generates a multilooked image from Single Look Complex (SLC) data. This step squares up the pixels so that the data begins to look like a typical image.
Transforms radar coordinates (from either ground or slant range) into a given cartographic reference system using a rigorous range-Doppler approach for ellipsoidal or terrain geocoding, using nominal parameters or Ground Control Points. The radiometric calibration, which is performed during the geocoding, is based on the exploitation of the radar equation. In addition, the radiometric normalisation empirically corrects the effects of the incidence angle on sigma nought by applying a modified cosine correction.
Effortlessly combine SAR images (amplitude and/or coherence) or products (classification results) to obtain an entire coverage of the area with this automated workflow. When mapping large areas using SAR data, individual image frames are seamlessly joined together to create a consistent mosaic across the region. This method refines the radiometric variations by comparing the images to be mosaicked in the overlapping areas.
Makes pixel-by-pixel comparisons of a time series of data sets of the same region, using automatic coregistration functionality in ENVI SARscape. In cases where pixel sizes vary, spatial registration, and resampling is also done automatically with the coregistration workflow.
ENVI SARscape has tools for analysing your processed imagery to provide a unique layer of information to your data which in turn helps you make the most informed decisions possible.
Once the SAR data is in an image format, you can use all the available ENVI classification tools to identify and map features in the image.
Extracts different feature parameters from intensity images or image pairs in several ways, such as by calculating coherence from a pair of images to detect changes in features over time; by using the coefficient of variation to extract features based on texture; or by using a time series of images to calculate a ratio to better extract features from SAR data. The images produced during feature extraction are based on first order and time-series statistics and can also be used as inputs for classification or quantitative analysis.
Generates a coherence map derived from interferometric processing of image pairs to detect changes of the same area over time. Changes of coherence over time can be used to create activity maps to detect paths, roads, or meeting places used by vehicles, humans or animals during the interval of time between two images. Change detection tools are also used to detect changes in the amplitude signal of the SAR data over time.
ENVI SARscape allows you to create Digital Elevation Models (DEMs) and land displacement maps to understand the topography of an area and track any land shifts or movement in structures that may have occurred.
From the orbital parameters of two SLC datasets, there are a series of steps that use the phase information to process data. Interim steps include: the generation of an interferogram; creation of a coherence map; DEM flattening; phase to height conversion and geocoding; and, phase to displacement conversion and geocoding. This process culminates in the creation of either a DEM or a displacement map.
ENVI SARscape processes interferometric SAR data (2-pass interferometry, InSAR) to generate DEMs. During this process, an interim step involves the generation of a coherence map. Highly accurate DEMs can be generated using the interferometric module.
Exploits differential SAR interferometry techniques to analyse stacks of SAR data in order to extract small deformations over large areas. This is particularly useful in rural areas where few point targets exist but where large, correlated displacements occur over natural targets – for example, land-displacement from an earthquake or landslide.
Detect very small displacements (mm scale) such as building subsidence or cracks in a dam wall using very stable, man-made reflectors. This is particularly suited to urban areas with many point targets.
ENVI SARscape includes inversion algorithms and analytical models to retrieve the parameters of geophysical sources underlying, and responsible for, the observed displacement. These tools can be used for modeling earthquakes, volcanoes and other geophysical phenomena.
ENVI SARscape processes differential interferometric SAR data to create highly accurate land displacement and deformation maps. They can be used to identifying minute changes such as areas where vehicles have passed through or disturbances around a building following new activity or to track much larger-scale changes resulting from, say, an earthquake or landslide.
ENVI SARscape integrates point-based and area-based techniques to process interferometric stacks of SAR images to create highly accurate displacement maps. This combined approach analyses deformation phenomena affecting both natural features and man-made features related to natural or man-induced phenomena like volcanic or seismic activity, landslides, subsidence, building failures and more.
The polarimetric SAR interferometry processing capabilities of ENVI SARscape include a robust set of tools to allow you to work with virtually all commercially available polarimetric data.
Polarimetric Calibration Matrix
Obtains a more accurate estimate of the target scattering matrix by using default or custom polarimetric calibration parameters.
Estimates the real and theoretical co-polarised and cross-polarised signatures of point targets (such as corner reflectors). Residual calibration errors are also detected.
Combines co- and cross-polarised intensity data which can be used for further interpretation or classification.
Uses the default linearly-polarised dataset to synthesise a scattering matrix for any arbitrary polarisation orthogonal basis.
Provides coherent (Pauli decomposition) and incoherent (Entropy-Alpha-Anisotropy eigen decomposition) methods for scattering matrix decomposition. Coherent methods are better for local target characterisation; incoherent methods for distributed target characterisation.
An unsupervised classification that discriminates different scattering behaviors on the basis of an incoherent decomposition result.
Single Look Complex Coregistration
Performs 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.
Synthetic Phase Generation
Generates the phase component related to the acquisition geometry.
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 and providing corresponding interferograms and coherence maps.
Polarimetric Phase Difference / Interferogram Generation
Performs the Polarimetric Phase Difference using the HH and VV polarisation of the same acquisition, and generates interferograms using the same polarisation of the PolInSAR pair.
Synthetic Phase Flattening
Inteferogram flattening can be executed using the synthetic phase generated from a different polarisation of the same acquisition pair.
Polarimetric / Interferometric Coherence
Generates correlation/coherence images based on Polarimetric Phase Difference / Interferogram products.