Abstract
Hyperspectral images usually consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, the high dimensionality also has some disadvantages, such as the Hughes effect and a high storage demand. Band selection is an effective method to address these issues. However, most band selection algorithms are conducted with the high-dimensional band images, which will bring high computation complexity and may deteriorate the selection performance. In this paper, spatial feature extraction is used to reduce the dimensionality of band images and improve the band selection performance. The experiment results obtained on three real hyperspectral datasets confirmed that the spatial feature extraction-based approach exhibits more robust classification accuracy when compared with other methods. Besides, the proposed method can dramatically reduce the dimensionality of each band image, which makes it possible for band selection to be implemented in real time situations.
Highlights
Hyperspectral images usually consist of hundreds of spectral bands, and this abundant spectral information can be used to precisely characterize different land cover types [1]
A support vector machine (SVM) with radial basis function (RBF) is used as the classifier, and the parameters are optimized by fivefold cross-validation
Random sampling is commonly used to reduce the dimensionality of hyperspectral images, which have high dimensionality, before band selection
Summary
Hyperspectral images usually consist of hundreds of spectral bands, and this abundant spectral information can be used to precisely characterize different land cover types [1]. Spatial feature extraction is applied to the high-dimensional band images to improve the band selection performance. Linear discriminant analysis (LDA) is a method used to find a linear combination of features that characterizes or separates two or more classes of objects or events [5] Despite their great advances, PCA and LDA fail to address the high-order dependencies, and much of the information may be contained in the highorder relationships. In conventional band selection methods, bands are usually selected based on the original high-dimensional band images. Instead of random sampling, spatial feature extraction is introduced to reduce the dimensionality of band images and improve the performance of band selection.
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