Relationship Prior and Adaptive Knowledge Mimic Based Compressed Deep Network for Aerial Scene Classification

Abstract

The aerial scene classification is one of the major tasks in the remote sensing community that automatically labels the corresponding semantic categories of aerial images. Recently, a lot of methods based on deep neural networks have been proposed, in which hierarchical internal feature are extracted for representations. However, these presented methods often have complex structures and require large volume of memory, and a large number of labeled aerial scene images are difficult to obtain, hindering their implementation in practical applications. In this paper, we present the between-class similarity priori and adaptive knowledge mimic (BPKM) method for aerial scene classification. First, the method extracts the efficient prior relationship information of the scene images from large-scale network. Then, a compressed network is generated through learning the output and the intermediate representations of the large-scale network, and the compressed network achieves better feature description ability; in addition, an improved cross-entropy method with an adaptive threshold is applied to reduce the training time consumption. A large-scale data set (AID) and UC-Merced data set are considered for performance evaluation, and the experimental results indicate that the proposed method is about $24\times $ parameters saving compared to popular networks, e.g., AlexNet, and has outstanding classification performance in classes with similar features.