Hyperspectral image classification algorithm based on multi-feature dynamic ensemble

doi:10.13474/j.cnki.11-2246.2026.0817

Abstract

Abstract: [Purposes] In order to use spectral information and spatial information in hyperspectral remote sensing images，a multi-feature dynamic ensemble method (MDE)based on hyperspectral remote sensing is proposed. [Methods] Firstly，the algorithm extracts spectral features，Gabor features，LBP features and EMAPs features of hyperspectral remote sensing images.Then,according to the characteristics of each test sample，the best feature prediction results are dynamically selected to participate in the integrated decision-making.Finally，two datasets of AVIRIS (airbone visible infrared Imaging spectrometer)sensor are used to evaluate the performance of the proposed algorithm. [Findings] The results show that the overall accuracy of MDE algorithm is up to 96.89% in Salinas dataset and 94.99% in Indian Pines dataset. [Conclusions] Compared with other methods，MDE can provide excellent and stable classification results．

Key words: ensemble learning, multi-feature, dynamic ensemble, image classification

CLC Number:

P237

Xu Hongxin, Yu Yao. Hyperspectral image classification algorithm based on multi-feature dynamic ensemble[J]. Bulletin of Surveying and Mapping, 2026, 0(8): 117-124,136.

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URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2026.0817

http://tb.chinasmp.com/EN/Y2026/V0/I8/117

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