A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements
An approach combining the Hotelling T^{2} control method with a weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling T^{2} procedure is introduced to identify features corresponding to cha...
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| Veröffentlicht in: | IEEE sensors journal 2019-07, Vol.19 (14), p.5843-5850 |
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| creator | Zerrouki, Nabil Harrou, Fouzi Sun, Ying Hocini, Lotfi |
| description | An approach combining the Hotelling T^{2} control method with a weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling T^{2} procedure is introduced to identify features corresponding to changed areas. Nevertheless, T^{2} scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for imbalanced problems, has been successfully applied to the features of the detected pixels to recognize the type of change. The feasibility of the proposed procedure is verified using SZTAKI AirChange benchmark data. Results proclaim that the proposed detection scheme succeeds to effectively identify land cover changes. Also, the comparisons with other methods (i.e., neural network, random forest, support vector machine, and k -nearest neighbors) highlight the superiority of the proposed method. |
| doi_str_mv | 10.1109/JSEN.2019.2904137 |
| format | Article |
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Hotelling <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> procedure is introduced to identify features corresponding to changed areas. Nevertheless, <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for imbalanced problems, has been successfully applied to the features of the detected pixels to recognize the type of change. The feasibility of the proposed procedure is verified using SZTAKI AirChange benchmark data. Results proclaim that the proposed detection scheme succeeds to effectively identify land cover changes. Also, the comparisons with other methods (i.e., neural network, random forest, support vector machine, and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbors) highlight the superiority of the proposed method.]]></description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2019.2904137</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Agriculture ; Algorithms ; Change detection ; Feature extraction ; Feature recognition ; Identification methods ; Land cover ; Land cover change detection ; Machine learning ; Monitoring ; multi-date measurements ; multi-spectral sensors ; multivariate statistical approach ; Neural networks ; Radiometry ; random forest classification ; Remote sensing ; Sensors ; Support vector machines</subject><ispartof>IEEE sensors journal, 2019-07, Vol.19 (14), p.5843-5850</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-f2fb70cab0b6a41dd1db229f19b2e1de4781584368bdddafca68f91813753183</citedby><cites>FETCH-LOGICAL-c384t-f2fb70cab0b6a41dd1db229f19b2e1de4781584368bdddafca68f91813753183</cites><orcidid>0000-0001-6703-4270 ; 0000-0002-2138-319X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8664182$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8664182$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zerrouki, Nabil</creatorcontrib><creatorcontrib>Harrou, Fouzi</creatorcontrib><creatorcontrib>Sun, Ying</creatorcontrib><creatorcontrib>Hocini, Lotfi</creatorcontrib><title>A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description><![CDATA[An approach combining the Hotelling <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> control method with a weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> procedure is introduced to identify features corresponding to changed areas. Nevertheless, <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for imbalanced problems, has been successfully applied to the features of the detected pixels to recognize the type of change. The feasibility of the proposed procedure is verified using SZTAKI AirChange benchmark data. Results proclaim that the proposed detection scheme succeeds to effectively identify land cover changes. Also, the comparisons with other methods (i.e., neural network, random forest, support vector machine, and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbors) highlight the superiority of the proposed method.]]></description><subject>Agriculture</subject><subject>Algorithms</subject><subject>Change detection</subject><subject>Feature extraction</subject><subject>Feature recognition</subject><subject>Identification methods</subject><subject>Land cover</subject><subject>Land cover change detection</subject><subject>Machine learning</subject><subject>Monitoring</subject><subject>multi-date measurements</subject><subject>multi-spectral sensors</subject><subject>multivariate statistical approach</subject><subject>Neural networks</subject><subject>Radiometry</subject><subject>random forest classification</subject><subject>Remote sensing</subject><subject>Sensors</subject><subject>Support vector machines</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE9PwzAMxSsEEmPwARCXSJw74iRt0-Mo4586kLYhcavSxt2KaDqSDolvT8omTrbl37P1XhBcAp0A0PTmeTl7mTAK6YSlVABPjoIRRJEMIRHyeOg5DQVP3k-DM-c-qCeTKBkFbkrmqto0BkmOyprGrMNb5VCT6XZrO78idWdJrowmWfeNlmQbZdZI7rDHqm86Q96cF5EFtl2PZInmbxz4hdJN12Jvm4rMUbmdxRZN786Dk1p9Orw41HGwup-tsscwf314yqZ5WHEp-rBmdZnQSpW0jJUArUGXjKU1pCVD0CgSCZEUPJal1lrVlYplnYL05iMOko-D6_1Z7-Nrh64vPrqdNf5jwZgQPKJCMk_Bnqps55zFutjaplX2pwBaDNEWQ7TFEG1xiNZrrvaaBhH_eRnHAvzFX_i8dbU</recordid><startdate>20190715</startdate><enddate>20190715</enddate><creator>Zerrouki, Nabil</creator><creator>Harrou, Fouzi</creator><creator>Sun, Ying</creator><creator>Hocini, Lotfi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Hotelling <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> procedure is introduced to identify features corresponding to changed areas. Nevertheless, <inline-formula> <tex-math notation="LaTeX">T^{2} </tex-math></inline-formula> scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for imbalanced problems, has been successfully applied to the features of the detected pixels to recognize the type of change. The feasibility of the proposed procedure is verified using SZTAKI AirChange benchmark data. Results proclaim that the proposed detection scheme succeeds to effectively identify land cover changes. Also, the comparisons with other methods (i.e., neural network, random forest, support vector machine, and <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbors) highlight the superiority of the proposed method.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2019.2904137</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-6703-4270</orcidid><orcidid>https://orcid.org/0000-0002-2138-319X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Agriculture Algorithms Change detection Feature extraction Feature recognition Identification methods Land cover Land cover change detection Machine learning Monitoring multi-date measurements multi-spectral sensors multivariate statistical approach Neural networks Radiometry random forest classification Remote sensing Sensors Support vector machines |
| title | A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements |
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