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Open AccessArticle

Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

by 1,*, 1, 2,* and 3
1
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
2
Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
3
Mechanical and Electrical Engineering College, Hainan University, Haikou 570228, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2161; https://doi.org/10.3390/app9102161
Received: 31 March 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 27 May 2019
Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method. View Full-Text
Keywords: hyperspectral image manifold learning; dimensionality reduction; local homogeneity; irregular spatial dependency; multiscale superpixel segmentation; covariance feature; classification hyperspectral image manifold learning; dimensionality reduction; local homogeneity; irregular spatial dependency; multiscale superpixel segmentation; covariance feature; classification
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He, L.; Chen, X.; Li, J.; Xie, X. Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification. Appl. Sci. 2019, 9, 2161.

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