An Advanced Dirichlet Prior Network for Out-of-distribution Detection in Remote Sensing

Abstract

This article introduces a compressive sensing (CS)-based approach for increasing bistatic synthetic aperture radar (SAR) imaging quality in the context of a multiaperture acquisition. The analyzed data were recorded over an opportunistic bistatic setup including a stationary ground-based-receiver opportunistic C-band bistatic SAR differential interferometry (COBIS) and Sentinel-1 C-band transmitter. Since the terrain observation by progressive scans (TOPS) mode is operated, the receiver can record synchronization pulses and echoed signals from the scene during many apertures. Hence, it is possible to improve the azimuth resolution by exploiting the multiaperture data. The recorded data are not contiguous and a naive integration of the chopped azimuth phase history would generate undesired grating lobes. The proposed processing scheme exploits the natural sparsity characterizing the illuminated scene. For azimuth profiles recovery greedy, convex, and nonconvex CS solvers are analyzed. The sparsifying basis/dictionary is constructed using the synthetically generated azimuth chirp derived considering Sentinel-1 orbital parameters and COBIS position. The chirped-based CS performance is further put in contrast with a Fourier-based CS method and an autoregressive model for signal reconstruction in terms of scene extent limitations and phase restoration efficiency. Furthermore, the analysis of different receiver-looking scenarios conducted to the insertion in the processing chain of a direct and an inverse Keystone transform for range cell migration (RCM) correction to cope with squinted geometries. We provide an extensive set of simulated and real-world results that prove the proposed workflow is efficient both in improving the azimuth resolution and in mitigating the sidelobes.