Superpixel Based Dimension Reduction for Hyperspectral Imagery

This paper focuses on dimension reduction (DR) technique for hyperspectral image (HSI). In this paper, we proposed a superpixel-based linear discriminant analysis (SP-LDA) dimension reduction method for HSI classification. Pixels within a local spatial neighborhood are expected to have similar spectral curves and share the same class label. To fully exploit the spatial structure, superpixel segmentation is firstly introduced to generate the superpixel map, which can adaptively explore the neighborhood structure information. Moreover, we extend the SP-LDA algorithm by combining the extracted feature from spectral and spatial dimensions, which can fully exploit complementary and consistent information from both dimensions. The experimental results on two standard hyperspectral datasets confirm the superiority of the proposed algorithms.

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