Superpixel segmentation and machine learning classification algorithm for cloud detection in remote-sensing images
Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clo...
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| Veröffentlicht in: | Journal of engineering (Stevenage, England) England), 2019-10, Vol.2019 (20), p.6675-6679 |
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| container_end_page | 6679 |
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| container_issue | 20 |
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| container_title | Journal of engineering (Stevenage, England) |
| container_volume | 2019 |
| creator | Shi, Yueting Wang, Weijiang Gong, Qishu Li, Dingyi |
| description | Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method. |
| doi_str_mv | 10.1049/joe.2019.0240 |
| format | Article |
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The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method.</description><subject>bright buildings</subject><subject>cloud detection</subject><subject>clouds</subject><subject>feature extraction</subject><subject>geophysical image processing</subject><subject>geophysical techniques</subject><subject>gf-1 remote-sensing images</subject><subject>GF‐1 remote‐sensing images</subject><subject>grey-level sub-regions</subject><subject>high grey-level sub-regions</subject><subject>IET International Radar Conference (IRC 2018)</subject><subject>image classification</subject><subject>image segmentation</subject><subject>learning (artificial intelligence)</subject><subject>machine learning classification algorithm</subject><subject>multidimensional feature extraction</subject><subject>Ostu threshold</subject><subject>remote sensing</subject><subject>remote-sensing image processing</subject><subject>snow-covered lands</subject><subject>softmax regression classifier</subject><subject>superpixel segmentation</subject><subject>target shape</subject><subject>voting-based clustering ensemble</subject><issn>2051-3305</issn><issn>2051-3305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>DOA</sourceid><recordid>eNp9kDtPwzAUhSMEEggY2bMypFy_0noExFNIDMBsOfZ1cJXYlZ0K-Pe4FCEWmO7rO-dKp6pOCMwIcHm2jDijQOQMKIed6oCCIA1jIHZ_9fvVcc5LACCMU-DkoEpP6xWmlX_Hoc7YjxgmPfkYah1sPWrz6gPWA-oUfOhrM-icvfPmmxn6mPz0OtYupnKMa1tbnNB8XX2oE45xwiZjyBu5H3WP-ajac3rIePxdD6uX66vny9vm4fHm7vL8oTFMMNoI2RkGRuKiBeOQswU3LXOCz93CSmZtS1onKSEdF0522syZtp0wzIC1vEyH1d3W10a9VKtUvqcPFbVXX4uYeqXT5M2ASkoBbN51gnHOpRWStS0pZhSk1ka44tVsvUyKOSd0P34E1CZ_VfJXm_zVJv_Ct1v-zQ_48T-snu-v6MV1aSktwtOt0ONUsHUKJaI_nnwCGf6YMA</recordid><startdate>201910</startdate><enddate>201910</enddate><creator>Shi, Yueting</creator><creator>Wang, Weijiang</creator><creator>Gong, Qishu</creator><creator>Li, Dingyi</creator><general>The Institution of Engineering and Technology</general><general>Wiley</general><scope>IDLOA</scope><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>201910</creationdate><title>Superpixel segmentation and machine learning classification algorithm for cloud detection in remote-sensing images</title><author>Shi, Yueting ; Wang, Weijiang ; Gong, Qishu ; Li, Dingyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3532-59bc30c9e860cfe4384c63f547f8d93dd616f9211b45f9bac73adb5c3c0dd4c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>bright buildings</topic><topic>cloud detection</topic><topic>clouds</topic><topic>feature extraction</topic><topic>geophysical image processing</topic><topic>geophysical techniques</topic><topic>gf-1 remote-sensing images</topic><topic>GF‐1 remote‐sensing images</topic><topic>grey-level sub-regions</topic><topic>high grey-level sub-regions</topic><topic>IET International Radar Conference (IRC 2018)</topic><topic>image classification</topic><topic>image segmentation</topic><topic>learning (artificial intelligence)</topic><topic>machine learning classification algorithm</topic><topic>multidimensional feature extraction</topic><topic>Ostu threshold</topic><topic>remote sensing</topic><topic>remote-sensing image processing</topic><topic>snow-covered lands</topic><topic>softmax regression classifier</topic><topic>superpixel segmentation</topic><topic>target shape</topic><topic>voting-based clustering ensemble</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Yueting</creatorcontrib><creatorcontrib>Wang, Weijiang</creatorcontrib><creatorcontrib>Gong, Qishu</creatorcontrib><creatorcontrib>Li, Dingyi</creatorcontrib><collection>IET Digital Library (Open Access)</collection><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of engineering (Stevenage, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Yueting</au><au>Wang, Weijiang</au><au>Gong, Qishu</au><au>Li, Dingyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Superpixel segmentation and machine learning classification algorithm for cloud detection in remote-sensing images</atitle><jtitle>Journal of engineering (Stevenage, England)</jtitle><date>2019-10</date><risdate>2019</risdate><volume>2019</volume><issue>20</issue><spage>6675</spage><epage>6679</epage><pages>6675-6679</pages><issn>2051-3305</issn><eissn>2051-3305</eissn><abstract>Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method.</abstract><pub>The Institution of Engineering and Technology</pub><doi>10.1049/joe.2019.0240</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | bright buildings cloud detection clouds feature extraction geophysical image processing geophysical techniques gf-1 remote-sensing images GF‐1 remote‐sensing images grey-level sub-regions high grey-level sub-regions IET International Radar Conference (IRC 2018) image classification image segmentation learning (artificial intelligence) machine learning classification algorithm multidimensional feature extraction Ostu threshold remote sensing remote-sensing image processing snow-covered lands softmax regression classifier superpixel segmentation target shape voting-based clustering ensemble |
| title | Superpixel segmentation and machine learning classification algorithm for cloud detection in remote-sensing images |
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