{"id":978,"date":"2020-06-16T14:40:34","date_gmt":"2020-06-16T19:40:34","guid":{"rendered":"http:\/\/pages.charlotte.edu\/gang-chen\/?p=978"},"modified":"2020-06-16T14:40:34","modified_gmt":"2020-06-16T19:40:34","slug":"shadow-removal-paper-published-in-rse","status":"publish","type":"post","link":"https:\/\/pages.charlotte.edu\/gang-chen\/blog\/2020\/06\/16\/shadow-removal-paper-published-in-rse\/","title":{"rendered":"Shadow removal paper published in RSE"},"content":{"rendered":"\n<p>Zhang, Y., Chen, G., Vukomanovic, J., Singh K.K., Liu, Y., Holden, S., &amp; Meentemeyer, R.K. (2020). Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. <em>Remote Sensing of Environment<\/em>, 247, 111945.<\/p>\n\n\n\n<p><strong>Abstract <\/strong><\/p>\n\n\n\n<p>Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. <strong>In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient.<\/strong> The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules \u2013 Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 \u00d7 500 pixels each) containing various types of shadows and six major land-cover classes \u2013 building, tree, grass\/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.<\/p>\n\n\n\n<figure class=\"wp-block-gallery columns-1 is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\"><ul class=\"blocks-gallery-grid\"><li class=\"blocks-gallery-item\"><figure><img loading=\"lazy\" decoding=\"async\" width=\"625\" height=\"641\" src=\"https:\/\/pages.charlotte.edu\/gang-chen\/wp-content\/uploads\/sites\/184\/2020\/06\/Self-Cast-shadows.jpg\" alt=\"\" data-id=\"979\" data-full-url=\"https:\/\/pages.charlotte.edu\/gang-chen\/wp-content\/uploads\/sites\/184\/2020\/06\/Self-Cast-shadows.jpg\" data-link=\"https:\/\/pages.charlotte.edu\/gang-chen\/?attachment_id=979\" class=\"wp-image-979\" srcset=\"https:\/\/pages.charlotte.edu\/gang-chen\/wp-content\/uploads\/sites\/184\/2020\/06\/Self-Cast-shadows.jpg 625w, https:\/\/pages.charlotte.edu\/gang-chen\/wp-content\/uploads\/sites\/184\/2020\/06\/Self-Cast-shadows-293x300.jpg 293w\" sizes=\"auto, (max-width: 625px) 100vw, 625px\" \/><\/figure><\/li><\/ul><figcaption class=\"blocks-gallery-caption\">Five sample patches, i.e., building, road, water, and tree in model performance for self shadow versus cast shadow detection between three scenarios: (a) NAIP, (b) Ground truth, (c) SDM, (d) SCM, and (e) RSAM, respectively.<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Zhang, Y., Chen, G., Vukomanovic, J., Singh K.K., Liu, Y., Holden, S., &amp; Meentemeyer, R.K. (2020). Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping. Remote Sensing of Environment, 247, 111945. Abstract Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed [&hellip;]<\/p>\n","protected":false},"author":44,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[1],"tags":[],"class_list":["post-978","post","type-post","status-publish","format-standard","hentry","category-updates"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p2VSMp-fM","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/posts\/978","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/users\/44"}],"replies":[{"embeddable":true,"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/comments?post=978"}],"version-history":[{"count":1,"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/posts\/978\/revisions"}],"predecessor-version":[{"id":980,"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/posts\/978\/revisions\/980"}],"wp:attachment":[{"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/media?parent=978"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/categories?post=978"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pages.charlotte.edu\/gang-chen\/wp-json\/wp\/v2\/tags?post=978"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}