Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3049267
Keywords
Hyperspectral imaging; Dictionaries; Correlation; Support vector machines; Optimization; Minimization; Mathematical model; Band selection (BS); constraint; hyperspectral image classification (HSIC); low-rank representation (LRR)
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Funding
- National Nature Science Foundation of China [61971082, 61801075, 41801231]
- Fundamental Research Funds for the Central Universities [3132017124]
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This study proposes a semi-supervised method for band selection based on dual-constrained low-rank representation, which improves band description performance through super-pixel and imbalanced class-wise constraints, and achieves rapid selection efficiency by building clusters adaptively using graph theory.
Band selection (BS) aims to choose a salient subset implied sufficient information from the numerous bands, which supplies a significantly efficient way to alleviate the barrier of dimensionality disaster for hyperspectral image classification (HSIC). This letter develops a semisupervised BS approach based on dual-constrained low-rank representation BS (DCLRR-BS) with two regularizations for HSIC. To be specific, a low-rank representation model is first proposed with super-pixel and imbalanced class-wise constraints, which are explicitly integrated to improve the performance of the band description. Next, the clusters are built adaptively based on graph theory in an unsupervised manner to rapid selection efficiency. A selection criterion is last designed to highlight the prominent band of each subset cluster to fulfill the BS procedure. Experimental results conducted on four types of classifiers with two real hyperspectral image (HSI) data sets demonstrate that the proposed DCLRR-BS method performs well in the imbalanced HSIC area.
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