Band selection for hyperspectral imagery based on combination of genetic algorithm and ant colony algorithm
结合遗传算法和蚁群算法的高光谱图像波段选择

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.