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Remote Sens. 2019, 11(2), 121; https://doi.org/10.3390/rs11020121

Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis

1
Department of Electrical and Computer Engineering, University of Iceland, Skolabraut 3, 220 Hafnarfjordur, Iceland
2
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, D-09599 Freiberg, Germany
3
Department of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 6, 107 Reykjavik, Iceland
*
Author to whom correspondence should be addressed.
Received: 1 December 2018 / Revised: 6 January 2019 / Accepted: 7 January 2019 / Published: 10 January 2019
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Abstract

In this paper, we develop a hyperspectral feature extraction method called sparse and smooth low-rank analysis (SSLRA). First, we propose a new low-rank model for hyperspectral images (HSIs) where we decompose the HSI into smooth and sparse components. Then, these components are simultaneously estimated using a nonconvex constrained penalized cost function (CPCF). The proposed CPCF exploits total variation penalty, 1 penalty, and an orthogonality constraint. The total variation penalty is used to promote piecewise smoothness, and, therefore, it extracts spatial (local neighborhood) information. The 1 penalty encourages sparse and spatial structures. Additionally, we show that this new type of decomposition improves the classification of the HSIs. In the experiments, SSLRA was applied on the Houston (urban) and the Trento (rural) datasets. The extracted features were used as an input into a classifier (either support vector machines (SVM) or random forest (RF)) to produce the final classification map. The results confirm improvement in classification accuracy compared to the state-of-the-art feature extraction approaches. View Full-Text
Keywords: classification; constrained penalized cost function; feature extraction; hyperspectral image; low-rank; total variation; sparse features; smooth features classification; constrained penalized cost function; feature extraction; hyperspectral image; low-rank; total variation; sparse features; smooth features
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Rasti, B.; Ghamisi, P.; Ulfarsson, M.O. Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis. Remote Sens. 2019, 11, 121.

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