A Remote Sensing Image Classification Method Based on Evidence Theory and Neural Networks

摘要

Neural networks have been widely used in remote sensing image classification. In this paper, we exploited the spatial information of the image to decide the classification result and proposed a remote sensing image classification method based on D-S evidence theory and neural networks. First, the original image to be classified is smoothed with the smoothed image obtained. Next, a B-P neural network is used to train and classify the original image and its smoothed image separately. Next, the two classification results (decisions) of the B-P neural network are fused with evidence theory. Finally, the fused result is as the final classification result of the original image. Experiment results show that the new method is efficient and improves the classification accuracy largely.