Feature Sparsity in Convolutional Neural Networks for Scene Classification of Remote Sensing Image

Recently, the analysis of remote sensing images has attracted a lot of attention. In the domain of scene classification, deep learning methods, especially convolutional networks (CNNs), currently achieve the best results. Although the classification performance has reached a high level, there are still some factors limiting the improvement of classification accuracy. Based on obeservation of remote sensing scene images, we fing that some scenes are quite similar though they belong to different classes. To improve the classification performance between different scenes with similar characteristics, we propose a significant Feature Sparsity Layer that can be esaily embedded into various convolutional network architectures. The proposed layer can inhibit the confusing features meanwhile stress the discriminative features, and it is used to sparse the multi-layer feature map, which is extracted by the convolutional layers. The proposed method achieves the state-of-the-art results on three datasets UC Merced Land Use, Aerial Image Data and OPTIMAL-31, and competitive result on dataset WHU-RS19.