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

Abdi, Ghasem and Farhad, Samadzadegan and 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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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.

Item URL in elib:https://elib.dlr.de/115655/
Document Type:Article
Title:Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Abdi, Ghasemghasem.abdi (at) ut.ac.irUNSPECIFIED
Farhad, SamadzadeganUniversity of TeheranUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:22 August 2017
Journal or Publication Title:Journal of Applied Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1117/1.JRS.11.042604
Page Range:042604-1-042604-15
Publisher:Society of Photo-optical Instrumentation Engineers (SPIE)
Keywords:deep features; deep learning; hyperspectral imagery classification; softmax regression; spectral-spatial unsupervised feature learning; stacked sparse autoencoder
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:29 Nov 2017 16:15
Last Modified:31 Jul 2019 20:13

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