A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. Furthermore, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.
Sandia National Laboratories flew its Facility for Advanced RF and Algorithm Development X-Band (9.6-GHz center frequency), fully polarimetric synthetic aperture radar (PolSAR) in VideoSAR mode to collect complex-valued SAR imagery before, during, and after the sixth Source Physics Experiment's (SPE-6) underground explosion. The VideoSAR products generated from the data sets include 'movies' of single-and quad-polarization coherence maps, magnitude imagery, and polarimetric decompositions. Residual defocus, due to platform motion during data acquisition, was corrected with a digital elevation model-based autofocus algorithm. We generated and exploited the VideoSAR image products to characterize the surface movement effects caused by the underground explosion. Unlike seismic sensors, which measure local area seismic waves using sparse spacing and subterranean positioning, these VideoSAR products captured high-spatial resolution, 2-D, time-varying surface movement. The results from the fifth SPE (SPE-5) used single-polarimetric VideoSAR data. In this paper, we present single-polarimetric and fully polarimetric VideoSAR results while monitoring the SPE-6 underground chemical explosion. We show that fully polarimetric VideoSAR imaging provides a unique, coherent, time-varying measure of the surface expression of the SPE-6 underground chemical explosion. We include new surface characterization results from the measured PolSAR SPE-6 data via H/A/α polarimetric decomposition.
Synthetic aperture radar (SAR) is a remote sensing technology that can truly operate 24/7. It's an all-weather system that can operate at any time except in the most extreme conditions. Coherent change detection (CCD) in SAR can identify minute changes such as vehicle tracks that occur between images taken at different times. From polarimetric SAR capabilities, researchers have developed decompositions that allow one to automatically classify the scattering type in a single polarimetric SAR (PolSAR) image set. We extend that work to CCD in PolSAR images to identify the type change. Such as change caused by no return regions, trees, or ground. This work could then be used as a preprocessor for algorithms to automatically detect tracks.
Sandia National Laboratories (SNL) flew its Facility for Advanced RF and Algorithm Development (FARAD) X-Band (9.6 GHz center frequency), fully-polarimetric synthetic aperture radar (PolSAR) in VideoSAR-mode to collect complex-valued SAR imagery before, during, and after the fifth and sixth Source Physics Experiment's (SPE-5 and SPE-6) underground explosion. The results from the fifth Source Physics Experiment (SPE-5) used single-polarimetric VideoSAR data while SPE-6 used single and fully-polarimetric VideoSAR data. We show that SAR can provide surface change products indicative of disturbances caused by the underground chemical explosions. These are surface coherence measures, Po1SAR change signatures, and differential interferometric SAR (InSAR) height change.
In an effort to enhance image classification of terrain features in fully polarimetric SAR images, this paper explores the utility of combining the results of two state-of-the-art decompositions along with a semi-supervised classification algorithm to classify each pixel in an image. Each pixel is labeled either with a pre-determined classification label, or labeled as unknown.