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Incorporating interferometric coherence into lulc classification of airborne polsar-images using fully convolutional networks

Schmitz, Sylvia; Weinmann, Martin; Thiele, Antje

Abstract:
Inspired by the application of state-of-the-art Fully Convolutional Networks (FCNs) for the semantic segmentation of high-resolution optical imagery, recent works transfer this methodology successfully to pixel-wise land use and land cover (LULC) classification of PolSAR data. So far, mainly single PolSAR images are included in the FCN-based classification processes. To further increase classification accuracy, this paper presents an approach for integrating interferometric coherence derived from co-registered image pairs into a FCN-based classification framework. A network based on an encoder-decoder structure with two separated encoder branches is presented for this task. It extracts features from polarimetric backscattering intensities on the one hand and interferometric coherence on the other hand. Based on a joint representation of the complementary features pixel-wise classification is performed. To overcome the scarcity of labelled SAR data for training and testing, annotations are generated automatically by fusing available LULC products. Experimental evaluation is performed on high-resolution airborne SAR data, captured over the German Wadden Sea. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000125304
Veröffentlicht am 27.10.2020
Originalveröffentlichung
DOI: 10.5194/isprs-archives-XLIII-B1-2020-115-2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1682-1750
KITopen-ID: 1000125304
Erschienen in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020, 2020, XXIV ISPRS Congress (2020 edition). Ed.: N. Paparoditis
Verlag ISPRS
Seiten 115-122
Serie The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 43
Schlagwörter LULC classification, Airborne PolSAR, Interferometric Coherence, Fully Convolutional Network
Nachgewiesen in Scopus
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