Affiliation:
1. The Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Abstract
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.
Funder
Natural Science Foundation of Liaoning 2022
Subject
General Earth and Planetary Sciences
Reference42 articles.
1. Secure Cloud-Aided Object Recognition on Hyperspectral Remote Sensing Images;Gao;IEEE Internet Things J.,2020
2. Li, H., Cui, J., Zhang, X., Han, Y., and Cao, L. (2022). Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction. Remote Sens., 14.
3. Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification;Bera;Int. J. Remote Sens.,2019
4. Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network;Lei;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2021
5. Constrained nonnegative matrix factorization and hyperspectral image dimensionality reduction;Xiao;Remote Sens. Lett.,2014