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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2023

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs15092315