원문정보
초록
영어
Hyperspectral image classification has received an increasing amount of interest in recent years. However, when representing pixels as vectors, the dimensionality of feature space is high, which causes “curse of dimensionality” problem. In this paper, in order to alleviate the impact of above problem, a manifold sparse coding method is proposed. Firstly, matrix decomposition technique is used to find a concept set and calculates relative data projection in the concept set. Secondly, manifold learning regularization is imported into objective function to capture the intrinsic geometric structure in the data. Finally, LASSO regularization is used to obtain sparse representation of data projection. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.
목차
1. Introduction
2. Hyperspectral Image Cube
3. Manifold Sparse Coding
3.1. Matrix Decomposition Technology
3.2. Manifold Learning Regularization
3.3. LASSO Regularization
4. Experimental Results
5. Conclusions
References