Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images
Geographic height information describes the vertical spatial structure of the city and serves as important foundational data for urban management. Obtaining height information from single-view remote sensing images is a relatively low-cost and convenient approach. However, there exist several bottle...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11048889/ |
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| author | Tao Zhang Furong Shi Yuanping Zhu |
| author_facet | Tao Zhang Furong Shi Yuanping Zhu |
| author_sort | Tao Zhang |
| collection | DOAJ |
| description | Geographic height information describes the vertical spatial structure of the city and serves as important foundational data for urban management. Obtaining height information from single-view remote sensing images is a relatively low-cost and convenient approach. However, there exist several bottlenecks in the current methods for inferring height information from monocular remote sensing images, such as difficulties in learning 3-D semantic information and accurately fitting the height morphology in various local scenes. In this study, we address these challenges by proposing a morphological-priors-guided network, termed MPG-Net, for accurate height estimation from single-view remote sensing images. First, considering the semantic morphological priors, we propose to explicitly enhance the 3-D visual cues (e.g., co-occurrence relationship between shadow buildings and shadow trees) and simultaneously design a semantic booster composed of a two-stream network with a multilevel cross-stream attention fusion mechanism to facilitate the 3-D feature learning for monocular height estimation. Second, taking into account the height distribution priors, we propose a scalable bins module that can create fully adaptive bins within a flexible height range for each input image, leading a more accurate delineation of height distribution pattern. The proposed MPG-Net is comprehensively evaluated on two datasets of different scenes (i.e., ISPRS Vaihingen and Potsdam datasets). Results indicate that the proposed MPG-Net significantly outperforms the existing methods, with the lowest root-mean-square error of 1.613 m and 1.947 m on Vaihingen and Potsdam, respectively. Furthermore, extensive ablation studies demonstrate the contribution of each designed component in the proposed method. |
| format | Article |
| id | doaj-art-d2f5635df24f4180becb2104425dc32f |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d2f5635df24f4180becb2104425dc32f2025-08-20T03:30:23ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118160531606510.1109/JSTARS.2025.358282311048889Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing ImagesTao Zhang0https://orcid.org/0000-0001-8236-8818Furong Shi1https://orcid.org/0000-0003-0750-954XYuanping Zhu2School of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin, ChinaSchool of Computer and Information Engineering, Tianjin Normal University, Tianjin, ChinaGeographic height information describes the vertical spatial structure of the city and serves as important foundational data for urban management. Obtaining height information from single-view remote sensing images is a relatively low-cost and convenient approach. However, there exist several bottlenecks in the current methods for inferring height information from monocular remote sensing images, such as difficulties in learning 3-D semantic information and accurately fitting the height morphology in various local scenes. In this study, we address these challenges by proposing a morphological-priors-guided network, termed MPG-Net, for accurate height estimation from single-view remote sensing images. First, considering the semantic morphological priors, we propose to explicitly enhance the 3-D visual cues (e.g., co-occurrence relationship between shadow buildings and shadow trees) and simultaneously design a semantic booster composed of a two-stream network with a multilevel cross-stream attention fusion mechanism to facilitate the 3-D feature learning for monocular height estimation. Second, taking into account the height distribution priors, we propose a scalable bins module that can create fully adaptive bins within a flexible height range for each input image, leading a more accurate delineation of height distribution pattern. The proposed MPG-Net is comprehensively evaluated on two datasets of different scenes (i.e., ISPRS Vaihingen and Potsdam datasets). Results indicate that the proposed MPG-Net significantly outperforms the existing methods, with the lowest root-mean-square error of 1.613 m and 1.947 m on Vaihingen and Potsdam, respectively. Furthermore, extensive ablation studies demonstrate the contribution of each designed component in the proposed method.https://ieeexplore.ieee.org/document/11048889/Deep learningheight estimationmorphological priorssingle-view remote sensing images |
| spellingShingle | Tao Zhang Furong Shi Yuanping Zhu Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning height estimation morphological priors single-view remote sensing images |
| title | Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images |
| title_full | Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images |
| title_fullStr | Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images |
| title_full_unstemmed | Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images |
| title_short | Morphological-Priors-Guided Network With Semantic Booster and Scalable Bins Module for Height Estimation From Single-View Remote Sensing Images |
| title_sort | morphological priors guided network with semantic booster and scalable bins module for height estimation from single view remote sensing images |
| topic | Deep learning height estimation morphological priors single-view remote sensing images |
| url | https://ieeexplore.ieee.org/document/11048889/ |
| work_keys_str_mv | AT taozhang morphologicalpriorsguidednetworkwithsemanticboosterandscalablebinsmoduleforheightestimationfromsingleviewremotesensingimages AT furongshi morphologicalpriorsguidednetworkwithsemanticboosterandscalablebinsmoduleforheightestimationfromsingleviewremotesensingimages AT yuanpingzhu morphologicalpriorsguidednetworkwithsemanticboosterandscalablebinsmoduleforheightestimationfromsingleviewremotesensingimages |