Unsupervised Rate Distortion Function-Based Band Subset Selection for Hyperspectral Image Classification

Due to significant inter-band correlation resulting from use of hundreds of contiguous spectral bands, band selection (BS) is one of most widely used methods to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands w...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Chang, Chein-I, Kuo, Yi-Mei, Hu, Peter Fuming
Format: Article
Language:English
Subjects:
Band selection (BS)
Band spectra
band subset selection (BSS)
Band theory
Channel coding
Classification
Compression
Criteria
Distortion
Entropy
Hyperspectral imaging
Image classification
Image coding
Image compression
Information theory
Iterative algorithms
Iterative methods
mutual information (MI)
rate distortion function (RDF)
Rate-distortion
Redundancy
Resource description framework
sequential rate distortion function BSS (SQ-RDFBSS)
Spectral bands
successive rate distortion function BSS (SC-RDFBSS)
Target detection
virtual dimensionality (VD)
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Summary:Due to significant inter-band correlation resulting from use of hundreds of contiguous spectral bands, band selection (BS) is one of most widely used methods to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with preserving crucial spectral information, while also avoiding selecting highly correlated bands. Information theory turns out to be one of best means to address such issue in terms of information redundancy, specifically, the rate distortion function (RDF) of Shannon's 3 rd noisy source coding (or joint source and channel coding) theorem, which has been widely used in image compression/coding. This paper presents a novel unsupervised RDF-based band subset selection (RDFBSS) for hyperspectral image classification (HSIC). To accomplish this goal, a new concept of the area under an RDF curve, A RDF similar to the area under a receiver operating characteristic (ROC), A z defined in hyperspectral target detection is coined and defined as a criterion for BSS. Since BSS generally requires an exhaustive search for an optimal band subset, two iterative algorithms similar to sequential (SQ) N-FINDR and successive (SC) N-FINDR for finding endmembers, called sequential (SQ) RDFBSS and successive (SC) RDFBSS, can be derived and coupled with Ardf as a criterion to find optimal band subsets. The experimental results demonstrate that RDFBSS is indeed a very effective BS method to find best possible band subsets and also performs better than most recent BS methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3296728