Learning Region Response Ranking Features for Remote Sensing Image Scene Classification

Abstract

Recently, deep learning especially convolutional neural networks (CNNs) has huge great success for remote sensing image scene classification. However, global CNN features still lack geometric invariance for addressing the problem of large intra-class variations and so are not optimal for scene classification. In this paper, we introduce a new feature representation for scene classification, named region response ranking (3R) feature representations by using off-the-shelf CNN models. Specifically, by considering each cube pixel of a certain convolutional feature map as one image region, we jointly train a class-specific support vector machine (SVM) base classifier and a decision function for each scene class. The base classifier is used to generate 3R feature by reordering the SVM responses of all image regions in descending order and the decision function is used for classification with 3R feature representations. Comprehensive evaluations on the publicly available NWPU-RESISC45 data set and comparisons with state-of-the-art methods demonstrate that the proposed 3R feature is effective for remote sensing image scene classification.1