Abstract

In the last decade, Hyperspectral imaging has become a very important source of remote sensing and industrial processing information through the acquisition of imaging data with a very high level of spectral detail. Although most of the traditional approaches for HSI analysis entail per-pixel spectral classification, spatial-spectral exploitation of HSI has the potential to further improve the classification performance—particularly when there is unique class-specific textural information in the scene. Since the dimensionality of such remotely sensed imagery is often very large, especially in spatial-spectral feature domain, a large amount of training data is required to accurately model the classifier. In this paper proposes a robust dimensionality reduction approach that effectively addresses this problem for hyperspectral imagery (HSI) analysis using spectral and spatial features. A new dimensionality reduction algorithm, SAGA-LFDA where a Self Adaptive Genetic Algorithm (SAGA) based feature selection and Local-Fishers Discriminant Analysis (LFDA) based feature projection are performed in a raw spectral-spatial feature space for effective dimensionality reduction. This is followed by a Spectral Angle Mapper (SAM) classifier. Classification results with experimental data show that our proposed method outperforms traditional dimensionality reduction and classification algorithms in challenging small training sample size and mixed pixel conditions.

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