Spectral-Locational-Spatial Manifold Learning for Hyperspectral Images Dimensionality Reduction

被引:7
|
作者
Li, Na [1 ]
Zhou, Deyun [1 ]
Shi, Jiao [1 ]
Wu, Tao [1 ]
Gong, Maoguo [2 ]
机构
[1] Northwestern Polytech Univ, Sch Elect & Informat, 127 West Youyi Rd, Xian 710072, Peoples R China
[2] Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ, Xian 710071, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
dimensionality reduction; hyperspectral images; manifold learning; classification; spectral-locational-spatial; LAPLACIAN EIGENMAPS; CLASSIFICATION;
D O I
10.3390/rs13142752
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.
引用
收藏
页数:25
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