Subdomain Style Compensation Network for Remote Sensing Cross-Domain Scene Classification

Abstract

High spatial resolution (HSR) imagery scene classification has been the subject of increased interest in recent years, and has great potential for many applications, such as urban planning and land cover classification. Deeping learning has been widely exploited in scene classification and achieved high classification accuracy. However, current the scene classification method based on deep learning depends on a large number of datasets for training, and the feature distribution of the default testing datasets is the same as that of the training datasets. In practical application, it is not only time-consuming and laborious to obtain a large number of training data, but also difficult to meet these needs due to the data shift between different domain. In addition, directly use of features extracted by the convolutional neural network (CNN) will lead to limited performance owing to the influence of domain migration. Therefore, how to effectively reduce the domain offset between the training data and the testing data, and discover discriminative information to scene classification is a challenging task. In this paper, the SSCN is proposed for HSR remote Sensing cross-domain scene classification. In SSCN, we introduce DSAN in which the local maximum mean discrepancy (LMMD) is first introduced for scene classification to capture the fine-grained information for each category and reduce the domain migration between training data and testing data. To extract more discriminative information from HSR images, a style normalization and restitution module (SNR) is developed, and an attention mechanism is added to the module to improve the performance of the model. The experimental results demonstrate that the proposed SSCN framework is superior to the state-of-the-art methods <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and performs well for cross-domain scene classification.