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Multi-objective evolutionary multi-tasking band selection algorithm for hyperspectral image classification.
- Source :
- Swarm & Evolutionary Computation; Oct2024, Vol. 90, pN.PAG-N.PAG, 1p
- Publication Year :
- 2024
-
Abstract
- Hyperspectral images (HSI) contain a great number of bands, which enable better characterization of features. However, the huge dimension and information volume brought by the abundant bands may give rise to a negative influence on the efficiency of subsequent processing on hyperspectral images. Band selection (BS) is a commonly adopted to reduce the dimension of HSIs. Different from the previous work, in this paper, hyperspectral band selection problem is formulated as a multi-objective optimization problem, where the band distribution uniformity among the selected bands and inter-class separation distance from a few labeled samples are optimized simultaneously. To fully exploit the relation between the band subsets with different sizes, we construct a multi-objective evolutionary multi-tasking algorithm for hyperspectral band selection (namely MEMT-HBS) to achieve the selected band subsets for all the selected band sizes in one run. To implement MEMT-HBS, the intra-task pairwise learning based solution generation strategy is suggested to evolve the population for each task to achieve high-quality offspring whose selected band size is restricted to a fixed scope. The inter-task band coverage based knowledge transferring strategy is utilized to choose useful individuals from adjacent tasks to further enhance the performance of current task. Compared with the state-of-the-art semi-supervised and unsupervised BS algorithms, empirical results on different standard hyperspectral datasets show that our proposed MEMT-HBS can determine the superior band subset which has a higher image classification accuracy over the comparison algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 22106502
- Volume :
- 90
- Database :
- Supplemental Index
- Journal :
- Swarm & Evolutionary Computation
- Publication Type :
- Academic Journal
- Accession number :
- 179062453
- Full Text :
- https://doi.org/10.1016/j.swevo.2024.101665