Multispectral remote sensing image classification based on PSO-BP considering texture

In recent years, back-propagation (BP) neural network has been widely applied to the remote sensing image classification. However, the BP method based on the gradient descent principle suffers from the problem of getting stuck at local minimum. In addition, only using spectral information for multispectral remote sensing image classification could not get the ideal result. In this paper, a new method which combines the feature texture knowledge with BP neural network trained by particle swarm optimization (PSO) is presented. The experimental results show that the proposed algorithm could not only improve the classification accuracy, but also increase the classification speed.