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| cloud detection based on segmentation with statistical and geometry features | |
| Li Bangyu; Li Xia | |
| 2012 | |
| Conference Name | 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 |
| Source | International Geoscience and Remote Sensing Symposium (IGARSS) |
| Pages | 6020-6023 |
| Conference Date | July 22, 2012 - July 27, 2012 |
| Conference Place | Munich, Germany |
| Indexed Type | EI |
| Department | (1) Institute of Software Chinese Academy of Sciences No. 4 South Fourth Street Haidian District Zhong Guan Cun Beijing China |
| English Abstract | Cloud detection, recognition has been received increasing attention during last decades in remote sensing application field. We propose a novel cloud detection algorithm based on statistical region merging segmentation with statistical and geometry features. To distinguish clouds objects from a background, the statistical region merging segmentation algorithm is firstly adopted to obtain semantic segmentation regions. Based on information of segmented patches, statistical features, including spectrum and geometry features are extracted to represent otherness between clouds and underlying surface. Such features are finally implied to math the feature temple by the nearest neighbor algorithm. We show in this paper the addressed method make a effective cloud detection without any prior constraints and auxiliary data. Experiments have been carried out on aerial optical images to validate our proposed method. © 2012 IEEE.; Cloud detection, recognition has been received increasing attention during last decades in remote sensing application field. We propose a novel cloud detection algorithm based on statistical region merging segmentation with statistical and geometry features. To distinguish clouds objects from a background, the statistical region merging segmentation algorithm is firstly adopted to obtain semantic segmentation regions. Based on information of segmented patches, statistical features, including spectrum and geometry features are extracted to represent otherness between clouds and underlying surface. Such features are finally implied to math the feature temple by the nearest neighbor algorithm. We show in this paper the addressed method make a effective cloud detection without any prior constraints and auxiliary data. Experiments have been carried out on aerial optical images to validate our proposed method. © 2012 IEEE. |
| Keyword | Geology Geometrical Optics Image Segmentation Merging Remote Sensing |
| Sponsorship | Geoscience and Remote Sensing Society (GRS) |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15835 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Li Bangyu,Li Xia. cloud detection based on segmentation with statistical and geometry features[C],2012:6020-6023. |
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