24 October 2017 Hyperspectral anomaly detection based on machine learning and building selection graph
Author Affiliations +
Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104625B (2017) https://doi.org/10.1117/12.2285780
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
In hyperspectral images, anomaly detection without prior information develops rapidly. Most of the existing methods are based on restrictive assumptions of the background distribution. However, the complexity of the environment makes it hard to meet the assumptions, and it is difficult for a pre-set data model to adapt to a variety of environments. To solve the problem, this paper proposes an anomaly detection method on the foundation of machine learning and graph theory. First, the attributes of vertexes in the graph are set by the reconstruct errors. And then, robust background endmember dictionary and abundance matrix are received by structured sparse representation algorithm. Second, the Euler distances between pixels in lower-dimension are regarded as edge weights in the graph, after the analysis of the low dimensional manifold structure among the hyperspectral data, which is in virtue of manifold learning method. Finally, anomaly pixels are picked up by both vertex attributes and edge weights. The proposed method has higher probability of detection and lower probability of false alarm, which is verified by experiments on real images.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yehui Tang, Yehui Tang, Hanlin Qin, Hanlin Qin, Ying Liang, Ying Liang, Hanbing Leng, Hanbing Leng, Zezhao Ju, Zezhao Ju, } "Hyperspectral anomaly detection based on machine learning and building selection graph", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104625B (24 October 2017); doi: 10.1117/12.2285780; https://doi.org/10.1117/12.2285780
PROCEEDINGS
6 PAGES


SHARE
Back to Top