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Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder

Abdi, Ghasem und Farhad, Samadzadegan und Reinartz, Peter (2017) Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder. Journal of Applied Remote Sensing, 11 (4), 042604-1-042604-15. Society of Photo-optical Instrumentation Engineers (SPIE). doi: 10.1117/1.JRS.11.042604. ISSN 1931-3195.

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Kurzfassung

Classification of hyperspectral remote sensing imagery is one of the most popular topics because of its intrinsic potential to gather spectral signatures of materials and provides distinct abilities to object detection and recognition. In the last decade, an enormous number of methods were suggested to classify hyperspectral remote sensing data using spectral features, though some are not using all information and lead to poor classification accuracy; on the other hand, the exploration of deep features is recently considered a lot and has turned into a Research hot spot in the geoscience and remote sensing research community to enhance classification accuracy. A deep learning architecture is proposed to classify hyperspectral remote sensing imagery by joint utilization of spectral-spatial information. A stacked sparse autoencoder provides unsupervised feature learning to extract high-level feature representations of joint spectral– spatial information; then, a soft classifier is employed to train high-level features and to fine-tune the deep learning architecture. Comparative experiments are performed on two widely used hyperspectral remote sensing data (Salinas and PaviaU) and a coarse resolution hyperspectral data in the long-wave infrared range. The obtained results indicate the superiority of the proposed spectral-spatial deep learning architecture against the conventional classification methods.

elib-URL des Eintrags:https://elib.dlr.de/115655/
Dokumentart:Zeitschriftenbeitrag
Titel:Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Abdi, Ghasemghasem.abdi (at) ut.ac.irNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Farhad, SamadzadeganUniversity of TeheranNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475NICHT SPEZIFIZIERT
Datum:22 August 2017
Erschienen in:Journal of Applied Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:11
DOI:10.1117/1.JRS.11.042604
Seitenbereich:042604-1-042604-15
Verlag:Society of Photo-optical Instrumentation Engineers (SPIE)
ISSN:1931-3195
Status:veröffentlicht
Stichwörter:deep features; deep learning; hyperspectral imagery classification; softmax regression; spectral-spatial unsupervised feature learning; stacked sparse autoencoder
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Verkehrsmanagement (alt)
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V VM - Verkehrsmanagement
DLR - Teilgebiet (Projekt, Vorhaben):V - Vabene++ (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Zielske, Mandy
Hinterlegt am:29 Nov 2017 16:15
Letzte Änderung:31 Jul 2019 20:13

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