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HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Image

Huang, Zhongling und Datcu, Mihai und Pan, Zongxu und Qiu, Xiaolan und Lei, Bin (2021) HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Image. IEEE Transactions on Geoscience and Remote Sensing, 59 (4), Seiten 3054-3071. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3014335. ISSN 0196-2892.

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Offizielle URL: https://ieeexplore.ieee.org/document/9169671

Kurzfassung

Understanding the physical properties and scattering mechanisms contributes to synthetic aperture radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the ``image'' domain. Based on TFA theory, we propose to generate the subband scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering (HDEC) algorithm based on TFA (HDEC-TFA) is proposed to learn the embedded features and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single-polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen-3 quad-polarized SAR data for experiments, and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex-valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications.

elib-URL des Eintrags:https://elib.dlr.de/138089/
Dokumentart:Zeitschriftenbeitrag
Titel:HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Image
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Huang, Zhonglinghuangzhongling15 (at) mails.ucas.ac.cnNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datcu, MihaiMihai.Datcu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Pan, ZongxuAerospace Information Research Institute, Chinese Academy of SciencesNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Qiu, XiaolanAerospace Information Research Institute, Chinese Academy of SciencesNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Lei, BinAerospace Information Research Institute, Chinese Academy of SciencesNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:April 2021
Erschienen in:IEEE Transactions on Geoscience and Remote Sensing
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:59
DOI:10.1109/TGRS.2020.3014335
Seitenbereich:Seiten 3054-3071
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:veröffentlicht
Stichwörter:Synthetic aperture radar,Scattering,Backscatter,Machine learning,Azimuth,Time-frequency analysis,Aerospace engineering
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - SAR-Methoden, R - Künstliche Intelligenz
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Karmakar, Chandrabali
Hinterlegt am:25 Nov 2020 16:48
Letzte Änderung:24 Aug 2021 16:10

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