30 March 2018 Integration of heterogeneous features for remote sensing scene classification
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Abstract
Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xin Wang, Xin Wang, Xingnan Xiong, Xingnan Xiong, Chen Ning, Chen Ning, Aiye Shi, Aiye Shi, Guofang Lv, Guofang Lv, } "Integration of heterogeneous features for remote sensing scene classification," Journal of Applied Remote Sensing 12(1), 015023 (30 March 2018). https://doi.org/10.1117/1.JRS.12.015023 . Submission: Received: 26 December 2017; Accepted: 13 March 2018
Received: 26 December 2017; Accepted: 13 March 2018; Published: 30 March 2018
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