Application of Hyperspectral Image Classification Based on Overlap Pooling

摘要

Convolutional neural networks (CNN) are increasingly being used in hyperspectral image (HSI) classification. However, most pooling methods are non-overlap pooling and ignore the influence of neighboring pixels on image characteristics, thereby limiting network classification accuracy. This work presents a deep CNN that is based on overlap pooling; in this model, non-overlap pooling is replaced with overlap pooling to improve the accuracy of feature extraction. However, overlap pooling introduces additional noise while improving feature accuracy. We have found that different combinations of max pooling and mean pooling can effectively solve the problem and significantly improve classification performance. The best pooling combination (max–mean–mean) for HSI classification is obtained after verification through experiments. A rectified linear unit activation function layer and the softmax loss classification model are combined to improve overall classification accuracy. Experiments on three HSI data sets, namely, Indian Pines, Salinas and Pavia University, show that the CNN model can increase overall accuracy to 95.66, 97.8 and 97.48%, respectively. Compared with deep network models such as deep belief network and non-overlap CNN, the proposed model has significantly improved the classification accuracy, and thus verifying the high accuracy of feature extraction of overlap pooling in CNN.