With the development of remote sensing technology and imaging spectrometer

hyperspectral remote sensing images are widely used. However

the features of hyperspectral images have brought great difficulties for its classification and identification. One important research question is "How to select a group of bands from hundreds of bands of hyperspectral images

which are good for classification and identification?" In view of the above question

the existing band selection methods are analyzed

and a new method of hyperspectral imagery band selection is proposed

which is combined with genetic algorithm and ant colony algorithm. In the algorithm

the genetic algorithm is used to search for some better solutions quickly which initialize the information list of the ant colony algorithm. Then

the ant colony algorithm can effectively search for the best solution. In the part of the genetic algorithm

quaternary encoding is used

which makes encoding/decoding and genetic operation simple and uses less memory. In the part of the ant colony algorithm

subspace division is used to deal with hyperspectral images

reducing the search range of the ants. Which improves the search efficiency

and reduces the correlation and redundancy of the output band of hyperspectral image. The algorithm makes good use of the advantages of both genetic algorithm and ant colony algorithm and overcomes their defects

by consuming less time and outperfoming restraining method for band selection. An AVIRIS image was used for experiment with the proposed algorithm

which proves that this algorithm of hyperspectal dimension reduction is effective in terms of band selection performance and execution time consumption.