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Open AccessArticle

SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification

1
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
2
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
3
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
4
College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 479; https://doi.org/10.3390/s19030479
Received: 19 November 2018 / Revised: 15 January 2019 / Accepted: 17 January 2019 / Published: 24 January 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become an important means to overcome the “Curse of dimensionality”. In hyperspectral images, labeled samples are more difficult to collect because they require many labor and material resources. Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly-labeled samples. The promotion of the supervised dimensionality reduction method to the semi-supervised method is mostly done by graph, which is a powerful tool for characterizing data relationships and manifold exploration. To take advantage of the spatial information of data, we put forward a novel graph construction method for semi-supervised learning, called SLIC Superpixel-based l 2 , 1 -norm Robust Principal Component Analysis (SURPCA2,1), which integrates superpixel segmentation method Simple Linear Iterative Clustering (SLIC) into Low-rank Decomposition. First, the SLIC algorithm is adopted to obtain the spatial homogeneous regions of HSI. Then, the l 2 , 1 -norm RPCA is exploited in each superpixel area, which captures the global information of homogeneous regions and preserves spectral subspace segmentation of HSIs very well. Therefore, we have explored the spatial and spectral information of hyperspectral image simultaneously by combining superpixel segmentation with RPCA. Finally, a semi-supervised dimensionality reduction framework based on SURPCA2,1 graph is used for feature extraction task. Extensive experiments on multiple HSIs showed that the proposed spectral-spatial SURPCA2,1 is always comparable to other compared graphs with few labeled samples. View Full-Text
Keywords: Hyperspectral Image; Robust Principal Component Analysis (RPCA); Simple Linear Iterative Clustering (SLIC); superpixel segmentation Hyperspectral Image; Robust Principal Component Analysis (RPCA); Simple Linear Iterative Clustering (SLIC); superpixel segmentation
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MDPI and ACS Style

Zu, B.; Xia, K.; Li, T.; He, Z.; Li, Y.; Hou, J.; Du, W. SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification. Sensors 2019, 19, 479.

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