UB-FineNet:用于开放获取卫星图像的城市建筑细粒度分类网络

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL
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引用次数: 0

摘要

利用卫星图像对城市规模的建筑物进行精细分类是一个重要的研究领域,对城市规划、基础设施建设和人口分布分析具有重大意义。然而,由于高空机载平台获取的俯拍图像分辨率低,城市建筑细粒度分类的样本分布呈长尾状,导致严重的类不平衡问题,因此这项任务面临巨大挑战。为了解决这些问题,我们提出了一种利用开放获取的卫星图像对城市建筑进行细粒度分类的深度网络方法。我们首先引入了基于去噪扩散概率模型(DDPM)的超分辨率方法来提高卫星图像的空间分辨率,这种方法得益于领域自适应知识提炼。然后,利用类别信息平衡模块(CIBM)和对比监督(CS)技术提出了一种新的细粒度分类网络,以缓解类别不平衡问题,提高分类的鲁棒性和准确性。在包含 11 种不同建筑类型的香港数据集上进行的实验表明,分类结果很有前途,Top-1 的平均准确率为 60.45%,与基于街景图像的方法相当。一项全面的消融研究表明,与基线方法相比,CIBM 和 CS 模块的 Top-1 准确率分别提高了 2.6% 和 3.5%。此外,这些模块还可以轻松集成到其他分类网络中,实现类似的性能改进。这项研究为仅使用开放获取的卫星图像对复杂特大城市环境中的建筑物进行详细分类提供了有效的解决方案,从而推进了城市分析。所提出的技术可作为城市规划者的宝贵工具,帮助了解城市和区域内的经济、工业和人口分布情况,最终促进城市发展和基础设施规划方面的明智决策。数据和代码将在以下网址公开发布
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
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.
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