Gravitational Search Optimized Hyperspectral Image Classification with Multilayer Perceptron

Hyperspectral image classification has been widely used in a variety of applications such as land cover analysis, mining, change detection and disaster evaluation. As one of the most-widely used classifiers, the Multilayer Perception (MLP) has shown impressive classification performance. However, the MLP is very sensitive to the setting of the training parameters such as weights and biases. The traditional parameter training methods, such as, error back propagation algorithm (BP), are easily trapped into local optima and suffer premature convergence. To address these problems, this paper introduces a modified gravitational search algorithm (MGSA) by employing a multi-population strategy to let four sub-populations explore the different areas in search space and a Gaussian mutation operator to mutate the global best individual when swarm stagnate. After that, MGSA is used to optimize the weights and biases of MLP. The experimental results on a public dataset have validated the higher classification accuracy of the proposed method.