Paper Info
Title
Dimensionality Reduction Of Hyperspectral Image Based On Local Constrained Manifold Structure Collaborative Preserving Embedding
Abstract
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.
Year
DOI
Venue
2021
10.3390/rs13071363
REMOTE SENSING
Keywords
DocType
Volume
hyperspectral image, graph learning, dimensionality reduction, collaborative representation, local neighborhood structure
Journal
13
Issue
Citations 
PageRank 
7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Guangyao Shi100.34
Fulin Luo2345.85
Yiming Tang301.69
Yuan Li421.37