Genetic algorithm-based parameter optimization for EO-1 Hyperion remote sensing image classification

Because hyperspectral remote sensing images have high band dimension and limited training samples, it is hard to achieve acceptable classification results using conventional statistical pattern recognition methods. Nevertheless, support vector machine (SVM) is suitable for hyperspectral image classification owing to its good generalization ability and minimal structural risk. Aimed to address the problems of large subjectivity, excessive time consumption, and low optimization accuracy in traditional SVM parameter selection, we applied genetic algorithm to optimize the model parameters. Using EO-1 Hyperion data as an example, an SVM object classification algorithm was proposed based on genetic algorithm optimization. To determine the effectiveness and superiority of this algorithm, a comprehensive evaluation and comparative analysis were performed with the classification effects of the cross-validation and maximum likelihood methods, respectively. Experimental results demonstrated that the proposed algorithm can effectively avert the aimlessness of artificial parameter selection and automatically optimize the SVM model parameters. It achieved relatively high overall classification accuracy (91.23%), which was 16.42% and 4.48% higher than the maximum likelihood method and cross-validation method, respectively.