Supervised feature extraction method based on low-rank representation with preserving local pairwise constraints for hyperspectral images

Feature extraction (FE) methods based on low-rank representation (LRR) have become important topics in hyperspectral images (HSIs) data analysis. In this paper, a supervised FE method for HSIs data based on LRR with the ability to preserve the local pairwise constraints information (LRLPC) is propos...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2019-04, Vol.13 (3), p.583-590
Main Authors: Ahmadi, Seyyed Ali, Mehrshad, Nasser, Razavi, Seyyed Mohammad
Format: Article
Language:English
Subjects:
Computer Imaging
Computer Science
Data analysis
Feature extraction
Finite element method
Fitness
Hyperspectral imaging
Image Processing and Computer Vision
Kernels
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Regularization
Representations
Signal,Image and Speech Processing
Vision
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Summary:Feature extraction (FE) methods based on low-rank representation (LRR) have become important topics in hyperspectral images (HSIs) data analysis. In this paper, a supervised FE method for HSIs data based on LRR with the ability to preserve the local pairwise constraints information (LRLPC) is proposed. LRLPC does not change the data dimensionality and only employs a technique to enrich the original feature space (OFS) and to obtain enriched feature space, which results in features richer than OFS. To overcome the problem of LRR in lacking the local structure information (LSI) of data, a local discriminative regularization term is imposed on the fitness function of LRR to keep the LSI of data. For nonlinear structure of data, LRLPC is extended to kernel LRLPC (KLRLPC) using kernel trick. Utilization of existing information in the pairwise constraints is useful for limited labeled samples situations as a common problem in HSI data analysis. The obtained experimental results using two well-known HSI data sets confirm the effectiveness of LRLPC and KLRLPC for dimension reduction and classification of HSIs.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-018-1385-7