Research On The Application Of Ant Colony Algorithm In The Dimentionality Reduction And Classification For Hyperspectral Image

The applications of hyperspectral remote sensing data have become broader and broader for its providing the abundant surface information. It is an important issue for the researchers how to extract useful information from the observed data, how to fully utilize such large amount of information, and how to make them become availability to our applications. Dimensionality reduction and classification are two key techniques in the hyperspectral image processing. Whether in hyperspectral image dimensionality reduction or in classification, both of them are the extraction of required information from a large number of hyperspectral data. And the integration of the extracted information is a process of combinatorial optimization. Ant colony optimization algorithm (ACO) is a new type of bionic optimization algorithm developed in recent years. Its main characteristics are self-organization, robustness, parallelism, which are very suitable for solving non-deterministic discrete combinatorial optimization problem. Based on the hyperspectral image characteristics, such as spectral resolution, spatial correlation, spectrum correlation, data dimensionality, information capacity, etc, the applications of ant colony algorithm to the processing are researched, and it is proven that the ant colony algorithm is suitable for hyperspectral image dimensionality reduction and classification in this dissertation.Dimensionality reduction of hyperspectral images includes band selection and feature extraction. In this dissertation, an ant colony algorithm based band seclection method of hyperspectral image is proposed to solve the complication of optimal algorithm and the computation load. In the new method, each band is regard as a node that an ant passes through during its foraging, the different evaluation function is chosen as a measure that ants select the path, and the optimal combination of bands is searched and obtained by using ant colony algorithm. The process of ants searching optimal path is the process of forming the optimal band combination. By the ant colony algorithm, several signature bands reflecting the material spectral distribution are selected from the whole spectral bands, and the band subspaces of reduced dimensionality are formed, in which the purpose for the dimensionality reduction of hyperspectral data is reached. Then, according to the characteristics that highly correlated specral bands are presented in group, a method of subspace decomposition to hyperspectral image is proposed based on ant colony algorithm. The new method adopts the feature transform to reduce the dimensionality of hyperspectral feature space. Also every band is regarded as a node that an ant passes through during its foraging. The ant judges its path in terms of the correlation between the bands. High-dimensional hyperspectral data space is decomposed into several lower-dimensional data sub-space by ant optimal searching process. Then by using the principal component analysis to extract the effective features of subspace, the dimensionality reduction of hyperspectral images is achieved.There are two kinds of classification method of hyperspectral image, i.e. supervising classification and unsupervising classification. A hyperspectral image classification method based on ant colony algorithm is proposed in this dissertation. Firstly, in terms of the information entropy of image, the gray-scale of every band in hyperspectral image is dispersed in partition. Then these condition items of discrete gray-scale are combined and a data set of condition items is formed. In the training samples, the condition terms formed by dispersing the hyperspectral image data is regarded as the candidate nodes of ants. The information entropy of condition items is regarded as heuristic function of ant transferring path. After an iteration searching of ant, every ant can construct a classification rule, and the best rule is reserved while the worse rule is gradually eliminated by adjusting the concentration of pheromone. After all training samples is classified, the classification rule is formed.Finally, an ant colony clustering algorithm is proposed based on ant chemical recognition system in this clustering algorithm. According to the similarity between ants, the ownership of ants is decided, and the ants being a high degree of similarity can assembled into a class. Each pixel in the remote sensing images is considered as an ant, in which besides the spectral information of every pixel in every band, the category labels, category attributes and so on are also included. In this dissertation, adopting the results acquired by the band selection of hyperspectral image based on ACO, the feature bands are extracted as simulation data, and the clustering method experiment is performed. The experiment results are compared with the results of traditional k-means algorithm. In order to objectively evaluate the performance of the clustering results, the clustering performance of the new algorithm is taken into account in the dissertation, which is intra-class distance, inter-class distance, and the correlation between clustering image and standard image. An objectively comprehensive evaluation measure that integrates the above parameters is proposed. The objective assessment to the new clustering algorithm and traditional k-means algorithm are acquired.