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

Gawlikowski, Jakob and Saha, Sudipan and Kruspe, Anna and Zhu, Xiao Xiang (2022) An Advanced Dirichlet Prior Network for Out-of-distribution Detection in Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, p. 8006305. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2022.3140324. ISSN 0196-2892.

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Official URL: https://ieeexplore.ieee.org/document/9668955

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.

Item URL in elib:https://elib.dlr.de/146186/
Document Type:Article
Title:An Advanced Dirichlet Prior Network for Out-of-distribution Detection in Remote Sensing
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Gawlikowski, JakobJakob.Gawlikowski (at) dlr.deUNSPECIFIED
Saha, Sudipansudipan.saha (at) tum.deUNSPECIFIED
Kruspe, AnnaTU MünchenUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:January 2022
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2022.3140324
Page Range:p. 8006305
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Dirichlet Network, Machine Learning, AI4EO, Remote Sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Institute of Data Science > Datamangagement and Analysis
Deposited By: Rösel, Anja
Deposited On:26 Nov 2021 09:16
Last Modified:28 Jan 2022 11:20

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