Hyperspectral Dimensionality Reduction of Forest Types Based on Cat Swarm Algorithm

One of the main ways of dimensionality reduction of hyperspectral image was band selection. The paper proposed a hyperspectral image bands selection method based on binary cat swarm algorithm to solve problems of the high complexity and intensive computation efficiently for follow-up applied research. In this paper, Jilin Wangqing Forestry Bureau was chosen as the study area, by optimization process of the cats’ location electing, less associated and more informative bands were selected from 115 bands of HJ-1A, band combination (22,37,109), to distinguish 5 kinds of dominant tree species and get better classification accuracy.