Remote Sensing Applications and Decision Support

Hull vector-based incremental learning of hyperspectral remote sensing images

[+] Author Affiliations
Fenghua Huang

Fuzhou University, Postdoctoral Programme of Electronic Science and Technology, Fuzhou 350116, China

Yango College, Fuzhou 350015, China

Luming Yan

Fujian Normal University, College of Geographical Sciences, Fuzhou 350007, China

J. Appl. Remote Sens. 9(1), 096022 (Aug 13, 2015). doi:10.1117/1.JRS.9.096022
History: Received December 3, 2014; Accepted July 15, 2015
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Abstract.  To overcome the inefficiency of incremental learning for hyperspectral remote sensing images, we propose a binary detection theory-sequential minimal optimization (BDT-SMO) nonclass-incremental learning algorithm based on hull vectors and Karush-Kuhn-Tucker conditions (called HK-BDT-SMO). This method can improve the accuracy and efficiency of BDT-SMO nonclass-incremental learning for fused hyperspectral images. But HK-BDT-SMO cannot effectively solve class-incremental learning problems (an increase in the number of classes in the newly added sample sets). Therefore, an improved version of HK-BDT-SMO based on hypersphere support vector machine (called HSP-BDT-SMO) is proposed. HSP-BDT-SMO can substantially improve the accuracy, scalability, and stability of HK-BDT-SMO class-incremental learning. Ultimately, HK-BDT-SMO and HSP-BDT-SMO are applied to the classification of land uses with fused hyperspectral images, and the classification results are compared with other incremental learning algorithms to verify their performance. In nonclass-incremental learning, the accuracy of HSP-BDT-SMO and HK-BDT-SMO is approximately the same and is higher than the others, and the former has the best learning speed; while in class-incremental learning, HSP-BDT-SMO has a better accuracy and more continuous stability than the others and the second highest learning speed next to HK-BDT-SMO. Therefore, HK-BDT-SMO and HSP-BDT-SMO are excellent algorithms which are respectively suitable to nonclass and class-incremental learning for fused hyperspectral images.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Fenghua Huang and Luming Yan
"Hull vector-based incremental learning of hyperspectral remote sensing images", J. Appl. Remote Sens. 9(1), 096022 (Aug 13, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.096022


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