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Remote Sensing
Volume 10
Issue 2
10.3390/rs10020284
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Article

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

by
Cong Wang
†,
Lei Zhang
†,
Wei Wei
* and
Yanning Zhang
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2018, 10(2), 284; https://doi.org/10.3390/rs10020284
Received: 6 December 2017 / Revised: 30 January 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
(This article belongs to the Special Issue Deep Learning for Target Object Detection and Identification in Remote Sensing Data)
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Abstract

When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework. View Full-Text
Keywords: deep unsupervised feature learning; segmented stacked denoising auto-encoder; low rank representation; hyperspectral imagery classification deep unsupervised feature learning; segmented stacked denoising auto-encoder; low rank representation; hyperspectral imagery classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style

Wang, C.; Zhang, L.; Wei, W.; Zhang, Y. When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature. Remote Sens. 2018, 10, 284. https://doi.org/10.3390/rs10020284

AMA Style

Wang C, Zhang L, Wei W, Zhang Y. When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature. Remote Sensing. 2018; 10(2):284. https://doi.org/10.3390/rs10020284

Chicago/Turabian Style

Wang, Cong, Lei Zhang, Wei Wei, and Yanning Zhang. 2018. "When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature" Remote Sensing 10, no. 2: 284. https://doi.org/10.3390/rs10020284

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