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Remote Sens. 2016, 8(2), 99; doi:10.3390/rs8020099

Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features

Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China
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Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li and Prasad Thenkabail
Received: 19 October 2015 / Revised: 20 December 2015 / Accepted: 30 December 2015 / Published: 27 January 2016
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Abstract

In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convolutional neural networks (CNN) to extract deep features from high levels of the image data. Deep features provide high level spatial information created by hierarchical structures. Although the deep features may have high dimensionality, they lie in class-dependent sub-spaces or sub-manifolds. We investigate the characteristics of deep features by using a sparse representation classification framework. The experimental results reveal that the proposed method exploits the inherent low-dimensional structure of the deep features to provide better classification results as compared to the results obtained by widely-used feature exploration algorithms, such as the extended morphological attribute profiles (EMAPs) and sparse coding (SC). View Full-Text
Keywords: deep learning; deep features; sparse representation; remote sensing image classification deep learning; deep features; sparse representation; remote sensing image classification
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Liang, H.; Li, Q. Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features. Remote Sens. 2016, 8, 99.

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