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HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON INTELLIGENT OPTIMIZATION FEATURE SELECTION
International journal of research - granthaalayah
Hyperspectral image classification has always been a hot topic. The problem of "dimension disaster" is caused by the high dimension of pixel points and the lack of labeled training sample points. In order to reduce the data dimension, an intelligent optimization algorithm was proposed for feature selection. The new method introduces the principle of mutual information and symmetric uncertainty, constructs the fitness function, selects the candidate feature set with the intelligent optimizationdoi:10.29121/granthaalayah.v8.i4.2020.14 fatcat:b4lv5qwt2fezfgsljv6gtbhptq