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Development of New Index Based Supervised Algorithm for Separation of Built-Up and River Sand Pixels from Landsat7 Imagery: Comparison of Performance with SVM
While extracting "built-up" pixels from satellite imagery, supervised classification algorithms often misclassify "river sand" pixels as "built-up" ones due to the similarity in their spectral profiles. With the help of the spectral reflectance information in BLUE &...
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| creator | Mukherjee, Amritendu Ramachandran, Parthasarathy |
| description | While extracting "built-up" pixels from satellite imagery, supervised classification algorithms often misclassify "river sand" pixels as "built-up" ones due to the similarity in their spectral profiles. With the help of the spectral reflectance information in BLUE & GREEN bands of Landsat satellite imagery, this study has introduced a new index BRSSI (Built-Up & River Sand Separation Index) that efficiently reduce the misclassification between these two classes. The results shows that average overall accuracy, F1 score and kappa ( \kappa ) coefficient for the developed index corresponding to selected 3 study regions across India are 0.9763, 0.9767 & 0.9527 respectively. |
| doi_str_mv | 10.1109/IGARSS46834.2022.9884652 |
| format | conference_proceeding |
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With the help of the spectral reflectance information in BLUE & GREEN bands of Landsat satellite imagery, this study has introduced a new index BRSSI (Built-Up & River Sand Separation Index) that efficiently reduce the misclassification between these two classes. The results shows that average overall accuracy, F1 score and kappa ( \kappa ) coefficient for the developed index corresponding to selected 3 study regions across India are 0.9763, 0.9767 & 0.9527 respectively.</description><identifier>EISSN: 2153-7003</identifier><identifier>EISBN: 9781665427920</identifier><identifier>EISBN: 1665427922</identifier><identifier>DOI: 10.1109/IGARSS46834.2022.9884652</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial satellites ; Built-Up & River Sand Separation ; Classification algorithms ; Earth ; Index based Methodology ; Landsat7 ; Machine Learning ; Reflectivity ; Rivers ; Satellites ; Support vector machines</subject><ispartof>IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, p.3183-3186</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9884652$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27899,54527,54904</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9884652$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mukherjee, Amritendu</creatorcontrib><creatorcontrib>Ramachandran, Parthasarathy</creatorcontrib><title>Development of New Index Based Supervised Algorithm for Separation of Built-Up and River Sand Pixels from Landsat7 Imagery: Comparison of Performance with SVM</title><title>IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium</title><addtitle>IGARSS</addtitle><description>While extracting "built-up" pixels from satellite imagery, supervised classification algorithms often misclassify "river sand" pixels as "built-up" ones due to the similarity in their spectral profiles. With the help of the spectral reflectance information in BLUE & GREEN bands of Landsat satellite imagery, this study has introduced a new index BRSSI (Built-Up & River Sand Separation Index) that efficiently reduce the misclassification between these two classes. The results shows that average overall accuracy, F1 score and kappa ( \kappa ) coefficient for the developed index corresponding to selected 3 study regions across India are 0.9763, 0.9767 & 0.9527 respectively.</description><subject>Artificial satellites</subject><subject>Built-Up & River Sand Separation</subject><subject>Classification algorithms</subject><subject>Earth</subject><subject>Index based Methodology</subject><subject>Landsat7</subject><subject>Machine Learning</subject><subject>Reflectivity</subject><subject>Rivers</subject><subject>Satellites</subject><subject>Support vector machines</subject><issn>2153-7003</issn><isbn>9781665427920</isbn><isbn>1665427922</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkNtOwkAQhlcTExF5Am_mBYp7aLe73gEqNkElVLwlK53FNT1lW04v47NaIlfz5c_M9ydDCDA6ZIzq-2Q6WqRpKJUIh5xyPtRKhTLiF2SgY8WkjEIea04vSY-zSAQxpeKa3DTNTweKU9ojv4-4w7yqCyxbqCy84R6SMsMDjE2DGaTbGv3OnXCUbyrv2u8CbOUhxdp407qqPJ2Nty5vg2UNpsxg4XbYLZxw7g6YN2B9VcCsCxrTxpAUZoP--ACTqugkrvl3zNF34sKUa4R91wPp5-stubImb3Bwnn2yfH76mLwEs_dpMhnNAseUaANpv6TKOF1zQZlFFhqdIVeccW2lsUjjWCiLikqm11lIszDjMVfC6LW0kbWiT-7-vQ4RV7V3hfHH1fmd4g9-pWyj</recordid><startdate>20220717</startdate><enddate>20220717</enddate><creator>Mukherjee, Amritendu</creator><creator>Ramachandran, Parthasarathy</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20220717</creationdate><title>Development of New Index Based Supervised Algorithm for Separation of Built-Up and River Sand Pixels from Landsat7 Imagery: Comparison of Performance with SVM</title><author>Mukherjee, Amritendu ; Ramachandran, Parthasarathy</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i183t-6fb68d20c2301fe14a9de282129f6afe07738fe80619cd40d4d27283a9c6f5ff3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial satellites</topic><topic>Built-Up & River Sand Separation</topic><topic>Classification algorithms</topic><topic>Earth</topic><topic>Index based Methodology</topic><topic>Landsat7</topic><topic>Machine Learning</topic><topic>Reflectivity</topic><topic>Rivers</topic><topic>Satellites</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Mukherjee, Amritendu</creatorcontrib><creatorcontrib>Ramachandran, Parthasarathy</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mukherjee, Amritendu</au><au>Ramachandran, Parthasarathy</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Development of New Index Based Supervised Algorithm for Separation of Built-Up and River Sand Pixels from Landsat7 Imagery: Comparison of Performance with SVM</atitle><btitle>IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium</btitle><stitle>IGARSS</stitle><date>2022-07-17</date><risdate>2022</risdate><spage>3183</spage><epage>3186</epage><pages>3183-3186</pages><eissn>2153-7003</eissn><eisbn>9781665427920</eisbn><eisbn>1665427922</eisbn><abstract>While extracting "built-up" pixels from satellite imagery, supervised classification algorithms often misclassify "river sand" pixels as "built-up" ones due to the similarity in their spectral profiles. With the help of the spectral reflectance information in BLUE & GREEN bands of Landsat satellite imagery, this study has introduced a new index BRSSI (Built-Up & River Sand Separation Index) that efficiently reduce the misclassification between these two classes. The results shows that average overall accuracy, F1 score and kappa ( \kappa ) coefficient for the developed index corresponding to selected 3 study regions across India are 0.9763, 0.9767 & 0.9527 respectively.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS46834.2022.9884652</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2153-7003 |
| ispartof | IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, p.3183-3186 |
| issn | 2153-7003 |
| language | eng |
| recordid | cdi_ieee_primary_9884652 |
| source | IEEE Xplore All Conference Series |
| subjects | Artificial satellites Built-Up & River Sand Separation Classification algorithms Earth Index based Methodology Landsat7 Machine Learning Reflectivity Rivers Satellites Support vector machines |
| title | Development of New Index Based Supervised Algorithm for Separation of Built-Up and River Sand Pixels from Landsat7 Imagery: Comparison of Performance with SVM |
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