Multi-Chromatic Analysis of SAR Images for Coherent Target Detection

Abstract

This work investigates the possibility of performing target analysis through the Multi-Chromatic Analysis (MCA), a technique that basically explores the information content of sub-band images obtained by processing portions of the range spectrum of a synthetic aperture radar (SAR) image. According to the behavior of the SAR signal at the different sub-bands, MCA allows target classification. Two strategies have been experimented by processing TerraSAR-X images acquired over the Venice Lagoon, Italy: one exploiting the phase of interferometric sub-band pairs, the other using the spectral coherence derived by computing the coherence between sub-band images of a single SAR acquisition. The first approach introduces the concept of frequency-persistent scatterers (FPS), which is complementary to that of the time-persistent scatterers (PS). FPS and PS populations have been derived and analyzed to evaluate the respective characteristics and the physical nature of the targets. Spectral coherence analysis has been applied to vessel detection, according to the property that, in presence of a random distribution of surface scatterers, as for open sea surfaces, spectral coherence is expected to be proportional to sub-band intersection, while in presence of manmade structures it is preserved anyhow. First results show that spectral coherence is well preserved even for very small vessels, and can be used as a complementary information channel to constrain

A New Detection Algorithm for Coherent Scatterers in SAR Data

“…The so-called interband coherence, based on a model for distributed scatterers, is then used to discriminate different types of targets. Since a performance analysis of this approach has been recently published in [12], no further investigations on interband coherence are included in this paper.…”

“…An alternative detection scheme that exploits the stability of the scattering along the spectral components in a pixel-based strategy has been presented as well, which is named the phase variance approach (PVA) [14], [15]. This method has been recently extended to interferometric pairs in [12] and [16]. However, no performance characterization of the SCA, the SEA, and the PVA is available yet.…”

“…A large bandwidth signal is the key factor since it allows finer spatial sampling and higher spectral diversity and simultaneously mitigates the resolution loss inherent to sublooking processing, which can be a drawback for some applications. The concept of interband coherence was introduced in [11], together with a coherence model for randomly distributed surface scatterers, which has been lately validated in [12]. If several overlapping sublooks are generated, a global average of the spectral coherence can be used to distinguish between different kinds of targets.…”

Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

“…Simple Linear Iterative Clustering (SLIC) is a widely used superpixel segmentation method [33], and has been introduced into some SAR scenes. Some researchers also use multi-chromatic analysis to perform target detection and analysis on SAR images [34,35].…”

Split-Band Interferometry-Assisted Phase Unwrapping for the Phase Ambiguities Correction

“…So far, the frequency-persistent scatterers have been selected using the multifrequency phase error [6] that is an estimator of the coherence from one spectral subband to another and that quantifies the quality of the phase measurements. In [7], the scattering properties of the frequency-persistent scatterers and "temporally" coherent scatterers (PS) from the Permanent Scatterers Interferometry (PSI) [8] are investigated. Among the potential applications of SBInSAR, let us mention absolute height retrieval, change detection [9], ionospheric correction [10,11] and urban monitoring.…”