Discriminative Spectral-Spatial Feature Extraction-Based Band Selection for Hyperspectral Image Classification

Recently, some spectral-spatial band selection (BS) strategies have become hugely popular as they fuse the spectral information of the pixels and the spatial relationship with the neighboring pixels to enhance the performance of classification methods. However, being unsupervised in nature, these me...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
Main Authors:	Baisantry, Munmun, Sao, Anil Kumar, Shukla, Dericks Praise
Format:	Article
Language:	eng
Subjects:	Band selection (BS) Classification component loadings Components Dimensionality reduction Embedding Feature extraction hyperspectral Hyperspectral imaging Image classification Information processing Linear programming Loading Methods neighborhood Objective function Pixels Principal component analysis principal component analysis (PCA) Principal components analysis Samples simple linear iterative clustering (SLIC) Spatial analysis spatial information Spectra superpixel Training
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Summary:	Recently, some spectral-spatial band selection (BS) strategies have become hugely popular as they fuse the spectral information of the pixels and the spatial relationship with the neighboring pixels to enhance the performance of classification methods. However, being unsupervised in nature, these methods do not utilize the class information of the training samples which could substantially empower the capabilities of such spectral-spatial BS methods. To circumvent this limitation, a supervised spectral-spatial BS method based on component loadings obtained from the principal components of spectral-spatial principal component analysis (PCA) and using a novel super-pixel based graph Laplacian embedding is proposed. The methodology attempts to unify the two strategies of dimensionality reduction, i.e., BS and feature extraction (FE), so that the benefits from both of them can be combined. The importance of each band is estimated in terms of its component loadings along the principal components which are estimated from a unified objective function consisting of three terms: data fidelity, classification error term, and spatial prior. Additionally, the spatial relationship among the neighboring samples is characterized using a novel superpixel-based graph model. An objective comparison of the proposed approach with several widely used, state-of-the-art BS methods demonstrates a significant improvement in the classification accuracy.
ISSN:	0196-2892 1558-0644