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Automatic detection of cloud in high-resolution remote sensing images based on adaptive SLIC and MFC
Kang, Chaomeng1,2; Liu, Jiahang1; Yu, Kai1,2; Lu, Zhuanli1,2
2017
Conference NameApplied Optics and Photonics China: Optical Sensing and Imaging Technology and Applications, AOPC 2017
Source PublicationAOPC 2017: Optical Sensing and Imaging Technology and Applications
Volume10462
Conference Date2017-06-04
Conference PlaceBeijing, China
PublisherSPIE
Contribution Rank1
Abstract

Reliable cloud detection plays an important role in the manufacture of remote sensing and the alarm of natural calamities. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of clouds with different concentration, color and shapes. Related works mostly used gray, shape and texture features to detect clouds, which obtain results with poor robustness and efficiency. To detect cloud more automatically and robustly, we propose a novel could detection method based on the fusion of local optimum by adaptive simple linear iterative clustering (ASLIC) and the whole optimum by bilateral filtering with an improved saliency detection method. After this step, we trained a multi-feature fusion model based support vector machine(SVM) used geometric feature: fractal dimension index (FRAC) and independence index (IDD) which is proposed by us to describe the piece of region's spatial distribution, texture feature: We use four angles to calculate the gray-level co-occurrence matrix (GLXM) about entropy, energy, contrast, homogeneity, spectral feature(SF): After principal component analysis(PCA) we choose the first bond, the second bond and the near infrared bond(NIR). Besides, in view of the disturbance of water, ice, we also use NDVI and HOT index to estimate the model. Compared to the traditional methods of SLIC,our new method for cloud detection is accurate, and robust when dealing with clouds of different types and sizes over various land satellite images. © 2017 SPIE.

Department遥感与智能信息系统研究中心
DOI10.1117/12.2285505
Indexed ByEI ; ISTP
ISBN9781510614055
Language英语
ISSN0277786X
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/29920
Collection遥感与智能信息系统研究中心
Affiliation1.Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an, 710119, China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
Recommended Citation
GB/T 7714
Kang, Chaomeng,Liu, Jiahang,Yu, Kai,et al. Automatic detection of cloud in high-resolution remote sensing images based on adaptive SLIC and MFC[C]:SPIE,2017.
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