Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

• Main Authors: Bao-Di Liu, Wen-Yang Xie, Jie Meng, Ye Li, Yanjiang Wang
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Remote Sensing
• Subjects: collaborative representation; hybrid collaborative representation; kernel space; remote-sensing images
• Online Access: https://www.mdpi.com/2072-4292/10/12/1934

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.