Methodology for Hyperspectral Band Selection
While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy. In this paper we present a new methodology
for combining unsupervised and supervised methods under classification accuracy and computational requirement constraints that is designed to perform hyperspectral band (wavelength range) selection and statistical modeling method selection. The band and method selections are utilized for prediction
of continuous ground variables using airborne hyperspectral measurements. The novelty of the proposed work is in combining strengths of unsupervised and supervised band selection methods to build a computationally efficient and accurate band selection system. The unsupervised methods are used
to rank hyperspectral bands while the accuracy of the predictions of supervised methods are used to score those rankings. We conducted experiments with seven unsupervised and three supervised methods. The list of unsupervised methods includes information entropy, first and second spectral
derivative, spatial contrast, spectral ratio, correlation, and principal component analysis ranking combined with regression, regression tree, and instance-based supervised methods. These methods were applied to a data set that relates ground measurements of soil electrical conductivity with
airborne hyperspectral image values. The outcomes of our analysis led to a conclusion that the optimum number of bands in this domain is the top four to eight bands obtained by the entropy unsupervised method followed by the regression tree supervised method evaluation. Although the proposed
band selection approach is demonstrated with a data set from the precision agriculture domain, it applies in other hyperspectral application domains.
Document Type: Research Article
Publication date: 01 July 2004
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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