Combining Multilevel Features for Remote Sensing Image Scene Classification With Attention Model

Abstract

Remote sensing (RS) image scene classification is a challenging task due to its intraclass variety and the interclass similarity. Recently, many convolutional neural network (CNN)-based methods explore the network to handle this task. However, RS images usually have confusing background in addition to the relevant objects, and features only derived from the whole RS images cannot achieve satisfying results. To solve the problem, this letter proposed a method of utilizing the attention network to localize multiscale discriminative regions of the RS scene images and combining features learned from the localized regions by a classification network. Specifically, the classification network is composed of three subnetworks, which are trained by certain scaled regions separately. To learn more discriminative feature representations, feature fusion module is introduced to fuse the features of the three subnetworks in a more effective way. Experiments conducted on the AID and NWPU-RESISC45 data sets evaluate the effectiveness of the proposed method.