{"title":"UB-FineNet:用于开放获取卫星图像的城市建筑细粒度分类网络","authors":"","doi":"10.1016/j.isprsjprs.2024.08.008","DOIUrl":null,"url":null,"abstract":"<div><p>Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at <span><span>https://github.com/ZhiyiHe1997/UB-FineNet</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50269,"journal":{"name":"ISPRS Journal of Photogrammetry and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":10.6000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"UB-FineNet: Urban building fine-grained classification network for open-access satellite images\",\"authors\":\"\",\"doi\":\"10.1016/j.isprsjprs.2024.08.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at <span><span>https://github.com/ZhiyiHe1997/UB-FineNet</span><svg><path></path></svg></span>.</p></div>\",\"PeriodicalId\":50269,\"journal\":{\"name\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS Journal of Photogrammetry and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0924271624003186\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS Journal of Photogrammetry and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0924271624003186","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
UB-FineNet: Urban building fine-grained classification network for open-access satellite images
Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.
期刊介绍:
The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive.
P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields.
In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.