Gl-Pooling: Global-Local Pooling For Hyper-Spectral Image Classification

As an important structure in the convolutional neural network, the pooling layer can effectively reduce the generalization of the improved features after convolution. However, the current pooling method is easy to lose the key low-frequency small sample data for the unbalanced hyperspectral image data, which leads to a decrease in the classification accuracy for such data. In this paper, we propose a pooling method, GL-pooling (global-local pooling), for the target category of hyperspectral samples. Calculating the joint probability of samples in the global and local pooling and maximizing the retention of small-sample detail information in the pooling process to improve the detection efficiency of the network for small targets. Concomitantly, the pooling method can be widely combined in many deep learning networks to solve the problem of difficult classification of small sample targets without increasing the network depth and the number of parameters. We achieve the best performance in two hyperspectral data sets.