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Article

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

by 1,2, 2,3, 2,3, 2,3, 2,3,4 and 2,3,*
1
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
2
Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu 610054, China
3
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
4
The Science and Technology on Optical Radiation Laboratory, Beijing 100854, China
*
Author to whom correspondence should be addressed.
Received: 25 November 2019 / Revised: 23 December 2019 / Accepted: 26 December 2019 / Published: 1 January 2020
(This article belongs to the Special Issue Computer Vision and Machine Learning Application on Earth Observation)
In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect. View Full-Text
Keywords: fractal dictionary learning; robust principal component analysis (RPCA); cirrus detection; infrared imagery fractal dictionary learning; robust principal component analysis (RPCA); cirrus detection; infrared imagery
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MDPI and ACS Style

Lyu, Y.; Peng, L.; Pu, T.; Yang, C.; Wang, J.; Peng, Z. Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery. Remote Sens. 2020, 12, 142. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010142

AMA Style

Lyu Y, Peng L, Pu T, Yang C, Wang J, Peng Z. Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery. Remote Sensing. 2020; 12(1):142. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010142

Chicago/Turabian Style

Lyu, Yuxiao, Lingbing Peng, Tian Pu, Chunping Yang, Jun Wang, and Zhenming Peng. 2020. "Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery" Remote Sensing 12, no. 1: 142. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010142

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