TRP-Oriented Hyperspectral Remote Sensing Image Classification Using Entropy-Weighted Ensemble Algorithm

The problem that the randomly generated random projection matrix will lead to unstable classification results is addressed in this paper. To this end, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. In particular, this paper presents a random projection matrix selection strategy based on the separable information of a single class able to project the features of a certain class of objects. The projection result is measured by the degree of separability, thereby obtaining the low-dimensional image with optimal separability of the class. After projecting samples with the same random projection matrix, to calculate the distance matrix, the Minimum Distance classifier is devised, repeating for all classes. Finally, the weight of the distance matrix is considered in ensemble classification by using the information entropy. The proposed algorithm is tested on real hyperspectral remote sensing images. The experiments show an increase in both stability and performance.

Automated summary

This paper addresses the problem of unstable classification results caused by randomly generated random projection matrix. To solve this problem, a Tighter Random Projection-oriented entropy-weighted ensemble algorithm is proposed for classifying hyperspectral remote sensing images. The algorithm selects a random projection matrix based on the separable information of a single class, and measures the projection result using the degree of separability to obtain the low-dimensional image with optimal separability. The Minimum Distance classifier is then used to calculate the distance matrix, and the weight of the distance matrix is considered in ensemble classification using information entropy.