{"created":"2020-11-20T03:59:53.595259+00:00","id":6324,"links":{},"metadata":{"_buckets":{"deposit":"86efd5b4-f4d6-44d8-bbc8-79f1ab81e27c"},"_deposit":{"created_by":45,"id":"6324","owner":"45","owners":[45],"owners_ext":{"displayname":"","email":"dimennyaung@uit.edu.mm","username":""},"pid":{"revision_id":0,"type":"recid","value":"6324"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/6324","sets":["1582963342780:1605779935331"]},"communities":["uit"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Object-based Urban Land Use Classification using Deep Belief Network","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Urban land use information is very important for urban\nplanning, regional administration and management.\nClassification of urban land use from high resolution\nimages remains a challenging task, due to the extreme\ndifficulties in differentiating complex spatial patterns to\nderive high-level semantic labels. Deep learning is a\npowerful state-of-the-art technique for image processing\nincluding remote sensing images. The Deep Belief\nNetworks (DBN) model is a widely investigated and\ndeployed deep learning architecture. It combines the\nadvantages of unsupervised and supervised learning and\ncan archive good classification performance. In this\npaper, deep belief network model is used to improve the\nperformance of object-based land use classification.\nFirst, to achieve an object-based image representation,\nthe original image is segmented into objects by graphbased\nminimal-spanning-tree segmentation algorithm.\nSecond, spectral, spatial and texture features for each\nobject are extracted. Then all features are put into deep\nbelief network and the parameters of the network using\ntraining samples are trained. Finally, all objects are\nclassified by network."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Classification"},{"interim":"Deep Belief Network"},{"interim":"Land Use"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2020-11-20"}],"displaytype":"preview","filename":"Object-based Urban Land Use Classification using Deep Belief Network.pdf","filesize":[{"value":"1.4 Mb"}],"format":"application/pdf","license_note":"© 2018 ICAIT","licensetype":"license_note","url":{"url":"https://meral.edu.mm/record/6324/files/Object-based Urban Land Use Classification using Deep Belief Network.pdf"},"version_id":"2c2a1150-d026-4ec8-8f6c-caaf2ed9fc2f"}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"ICAIT-2018","subitem_c_date":"1-2 November, 2018","subitem_conference_title":"2nd International Conference on Advanced Information Technologies","subitem_place":"Yangon, Myanmar","subitem_session":"Image Processing","subitem_website":"https://www.uit.edu.mm/icait-2018/"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Su Wai Tun"},{"subitem_authors_fullname":"Khin Mo Mo Tun"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Conference paper"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2018-11-02"},"item_title":"Object-based Urban Land Use Classification using Deep Belief Network","item_type_id":"21","owner":"45","path":["1605779935331"],"publish_date":"2020-11-20","publish_status":"0","recid":"6324","relation_version_is_last":true,"title":["Object-based Urban Land Use Classification using Deep Belief Network"],"weko_creator_id":"45","weko_shared_id":-1},"updated":"2021-12-13T04:26:40.172524+00:00"}