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Hyperspectral image classification using multiobjective optimization
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DOI：10.1007/s11042-022-12462-6.png)
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摘要
En 中文
Hyperspectral images constitute a substantial amount of data in the form of spectral bands. This information is used for land cover analysis, specifically in classifying a hyperspectral pixel, which is a popular domain in remote sensing. This paper proposed an efficient framework to classify spectral-spatial hyperspectral images by employing multiobjective optimization. Spectral-spatial features of hyperspectral images are passed for optimization. As hyperspectral images have a high dimensional feature set, many classifiers cannot perform well. Multiobjective optimization reduces the feature set without affecting the discrimination ability of the classifier. The proposed work is validated on a standard hyperspectral image set, Pavia University and Kennedy Space Centre.
Keyword:
Hyperspectral images
Classification
Multiobjective optimization
MOEAD
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
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