Dimensionality reduction method for hyperspectral images based on weighted spatial-spectral combined preserving embedding

doi:10.11947/j.AGCS.2019.20180229

Abstract

Abstract: Hyperspectral image (HSI) contains a large number of spectral bands, which easily leads to the curse of dimensionality. However, the traditional manifold learning methods generally only consider the spectral features, while the spatial information of HSI is ignored. To overcome this shortcoming, it is proposed that an unsupervised dimensionality reduction algorithm called weighted spatial-spectral combined preserving embedding (WSCPE) for HSI classification. Firstly, the proposed algorithm uses a weighted mean filter (WMF) to filter the image, which can reduce the influence of background noise. Then, according to the spatial consistency property of HSI, it adopts the weighted spatial-spectral combined distance (WSCD) to fuse the spectral and spatial information of pixels to effectively select the spatial-spectral neighbors of each pixel. Finally, the proposed method explores the coordinate distances between pixels and their spatial-spectral neighbors to perform manifold reconstruction, and the low-dimensional discriminative features are extracted for HSI classification. The experimental results on PaviaU and Indian Pines datasets indicate that the overall classification accuracies of the proposed method reached 98.89% and 95.47%, respectively. The WSCPE method not only discovers the intrinsic manifold structure of HSI data, but also effectively integrates the spatial-spectral combined information, which enhances the classification performance.

Key words: hyperspectral remote sensing image, manifold learning, dimensionality reduction, spatial-spectral neighbors, discriminant features

CLC Number:

P237

HUANG Hong, SHI Guangyao, DUAN Yule, ZHANG Limei. Dimensionality reduction method for hyperspectral images based on weighted spatial-spectral combined preserving embedding[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8): 1014-1024.

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