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Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet

文献类型：期刊论文

作者Li, Xuemei2; Ye, Huping3; Qiu, Shi1
刊名REMOTE SENSING
出版日期2022-10-01
卷号14期号:19页码:27
关键词remote sensing self-supervision multi-layer feature fusion multispectral
DOI10.3390/rs14194815
通讯作者Ye, Huping(yehp@igsnrr.ac.cn)
英文摘要Multispectral remote sensing images have shown unique advantages in many fields, including military and civilian use. Facing the difficulty in processing cloud contaminated remote sensing images, this paper proposes a multispectral remote sensing image enhancement algorithm. A model is constructed from the aspects of cloud detection and image enhancement. In the cloud detection stage, clouds are divided into thick clouds and thin clouds according to the cloud transmitability in multi-spectral images, and a multi-layer cloud detection model is established. From the perspective of traditional image processing, a bimodal pre-detection algorithm is constructed to achieve thick cloud extraction. From the perspective of deep learning, the MobileNet algorithm structure is improved to achieve thin cloud extraction. Faced with the problem of insufficient training samples, a self-supervised network is constructed to achieve training, so as to meet the requirements of high precision and high efficiency cloud detection under the condition of small samples. In the image enhancement stage, the area where the ground objects are located is determined first. Then, from the perspective of compressed sensing, the signal is analyzed from the perspective of time and frequency domains. Specifically, the inter-frame information of hyperspectral images is analyzed to construct a sparse representation model based on the principle of compressed sensing. Finally, image enhancement is achieved. The experimental comparison between our algorithm and other algorithms shows that the average Area Overlap Measure (AOM) of the proposed algorithm reaches 0.83 and the Average Gradient (AG) of the proposed algorithm reaches 12.7, which is better than the other seven algorithms by average AG 2.
WOS关键词HYPERSPECTRAL IMAGES ; CLASSIFICATION
资助项目National Key Research and Development Program of China[2019YFE0126500] ; National Science and Technology Major Project of China's High Resolution Earth Observation System[21-Y20B01-9001-19/22] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YJKYYQ20200010]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000867036700001
出版者MDPI
资助机构National Key Research and Development Program of China ; National Science and Technology Major Project of China's High Resolution Earth Observation System ; Scientific Instrument Developing Project of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/185527]  
专题中国科学院地理科学与资源研究所
通讯作者Ye, Huping
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China
2.Chengdu Univ Technol, Sch Mech & Elect Engn, Chengdu 610059, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Li, Xuemei,Ye, Huping,Qiu, Shi. Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet[J]. REMOTE SENSING,2022,14(19):27.
APA Li, Xuemei,Ye, Huping,&Qiu, Shi.(2022).Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet.REMOTE SENSING,14(19),27.
MLA Li, Xuemei,et al."Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet".REMOTE SENSING 14.19(2022):27.

入库方式： OAI收割

来源：地理科学与资源研究所

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