{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T02:34:21Z","timestamp":1775615661874,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory fund of Chinese Academy of Sciences","award":["CXJJ-23S032"],"award-info":[{"award-number":["CXJJ-23S032"]}]},{"name":"Key Laboratory fund of Chinese Academy of Sciences","award":["CXJJ-22S032"],"award-info":[{"award-number":["CXJJ-22S032"]}]},{"name":"the Key Laboratory fund of Chinese Academy of Sciences","award":["CXJJ-23S032"],"award-info":[{"award-number":["CXJJ-23S032"]}]},{"name":"the Key Laboratory fund of Chinese Academy of Sciences","award":["CXJJ-22S032"],"award-info":[{"award-number":["CXJJ-22S032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, with monocular height estimation offering wide-ranging data sources and convenient deployment. This paper addresses the existing challenges in monocular height estimation methods, namely the difficulty in simultaneously achieving high-quality instance-level height and edge reconstruction, along with high computational complexity. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification\u2013regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification\u2013regression Height Generator (ICG). MIB supplements the fixed sample grid in the CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification\u2013regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness. Finally, the study conducts experimental validations on the Vaihingen and Potsdam datasets, with results demonstrating that our proposed method surpasses existing techniques.<\/jats:p>","DOI":"10.3390\/rs16020295","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T05:24:12Z","timestamp":1704950652000},"page":"295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["HeightFormer: A Multilevel Interaction and Image-Adaptive Classification\u2013Regression Network for Monocular Height Estimation with Aerial Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhan","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]},{"given":"Yidan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]},{"given":"Xiyu","family":"Qi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9256-3668","authenticated-orcid":false,"given":"Yongqiang","family":"Mao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]},{"given":"Yunping","family":"Ge","sequence":"additional","affiliation":[{"name":"Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, CAS, Beijing 100094, China"},{"name":"Key Laboratory of Network Information System Technology, Aerospace Information Research Institute, CAS, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/JPROC.2012.2190811","article-title":"Very high-resolution remote sensing: Challenges and opportunities","volume":"100","author":"Benediktsson","year":"2012","journal-title":"Proc. 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