Deep Feature Reconstruction Learning for Open-Set Classification of Remote-Sensing Imagery
Hao Sun, Qianqian Li, Jie Yu, Dongbo Zhou, Wenjing Chen, Xiangtao Zheng, Xiaoqiang Lu
Abstract
Existing remote sensing scene image (RSSI) classification methods usually rely on static closed-set assumption that testing samples do not belong to unknown classes. However, practical applications are usually the open-set classification problem, which means that RSSIs from unknown classes will appear in the testing set. Most existing methods are prone to forcibly misclassify RSSIs of unknown classes into known classes, resulting in poor practical performance. In this letter, a deep feature reconstruction learning (DFRL) framework is proposed for open-set classification of RSSIs. The proposed DFRL unifies discriminative feature learning and feature reconstruction into an end-to-end network. Firstly, a feature extraction module is utilized to project raw input data from the image space to the feature space to extract deep features. Then, the deep features are fed to a deep feature reconstruction module for distinguishing known and unknown classes based on feature-level reconstruction errors. The feature-level reconstruction can effectively suppress the interference of complex backgrounds. In addition, a sparse regularization is introduced to improve the discrimination of image representation. Experiments on three RSSI datasets demonstrate the effectiveness of DFRL for open-set classification of RSSIs.