1.武汉大学遥感信息工程学院,湖北 武汉,430079
2.武汉大势智慧科技有限公司,湖北 武汉,430223
3.国家基础地理信息中心,北京,100830
4.华中师范大学人工智能教育学部,湖北 武汉,430079
5.武汉汉达瑞科技有限公司,湖北 武汉,430073
6.南京工业大学测绘科学与技术学院,江苏 南京,211816
7.浙江省测绘科学技术研究院,浙江 杭州,310012
8.自然资源浙江省卫星应用技术中心,浙江 杭州,310012
9.自然资源部地理国情监测重点实验室,浙江 杭州,310012
胡翔云,博士,教授,现从事人工智能、摄影测量与遥感方向研究。huxy@whu.edu.cn
李小凯,博士。lixiaokai@daspatial.com
收稿:2024-04-24,
纸质出版:2025-03-05
移动端阅览
胡翔云,张觅,张祖勋,等.遥感影像智能解译系统EasyFeature的关键技术及应用[J].武汉大学学报(信息科学版),2025,50(3):554-561.
HU Xiangyun,ZHANG Mi,ZHANG Zuxun,et al.Key Technologies and Applications of EasyFeature—An Intelligent Interpretation System for Remote Sensing Images[J].Geomatics and Information Science of Wuhan University,2025,50(3):554-561.
胡翔云,张觅,张祖勋,等.遥感影像智能解译系统EasyFeature的关键技术及应用[J].武汉大学学报(信息科学版),2025,50(3):554-561. DOI:10.13203/j.whugis20240159
HU Xiangyun,ZHANG Mi,ZHANG Zuxun,et al.Key Technologies and Applications of EasyFeature—An Intelligent Interpretation System for Remote Sensing Images[J].Geomatics and Information Science of Wuhan University,2025,50(3):554-561. DOI:10.13203/j.whugis20240159
随着大数据和人工智能技术的迅猛发展,遥感影像自动解译技术取得了显著进步,但现有遥感影像自动解译方法在鲁棒性、可靠性以及精度等方面仍难以与人相媲美。面向实际生产应用需求,创建了场景-目标-像素层次关系的多要素提取模型,形成了遥感影像分类和要素提取成套技术;提出了语义信息增强与虚警再抑制机制、融合先验形状、特征匹配优化、二维-三维联合处理等变化检测新方法,以及人机智能协同的交互式地物采编思路,构建了高性能遥感影像智能解译技术体系,研发了自主知识产权软件系统EasyFeature,并在全球测图、自然资源常态化监测等国家重大工程中取得了广泛应用,降低了中国对国外同类软件的依赖。
With the rapid development of big data and artificial intelligence technologies
significant progress has been achieved in automatic interpretation techniques for remote sensing imagery. However
the robustness
reliability
and accuracy of existing automatic interpretation methods still fall short of human-level performance when compared. To address the practical production application needs
this research has developed a multi-object extraction model that establishes a hierarchical relationship among scene-object-pixel
thereby creating a comprehensive suite of technologies for remote sensing image classification and feature extraction. It introduces novel methods for change detection
such as semantic information enhancement coupled with false alarm re-suppression mechanisms
fusion of prior shape knowledge
optimized feature matching
and 2D-3D joint processing. Furthermore
it proposes a conceptual framework for interactive land feature extraction and editing through human-computer intelligent collaboration
thus constructing a high-performance intelligent interpretation technology system for remote sensing imagery. This research has resulted in the development of an independent intellectual property software system named EasyFeature. And it has been widely applied in major national projects such as global mapping and normalized monitoring of natural resources
effectively reducing China's reliance on foreign comparable software solutions.
LI D R , WANG M , JIANG J . China’s High-Resolution Optical Remote Sensing Satellites and Their Mapping Applications [J]. Geo‐Spatial Information Science , 2021 , 24 ( 1 ): 85 - 94 .
张泽根 , 张拯宁 , 李媛 , 等 . 面向“十四五” 的卫星综合应用产业发展研究 [J]. 卫星应用 , 2020 ( 9 ): 31 - 38 .
ZHANG Zegen , ZHANG Zhengning , LI Yuan , et al . Research on the Development of Satellite Integrated Application Industry Facing the Tenth Five-Year Plan [J]. Satellite Application , 2020 ( 9 ): 31 - 38 .
陈玲 , 贾佳 , 王海庆 . 高分遥感在自然资源调查中的应用综述 [J]. 国土资源遥感 , 2019 , 31 ( 1 ): 1 - 7 .
CHEN Ling , JIA Jia , WANG Haiqing . An Overview of Applying High Resolution Remote Sensing to Natural Resources Survey [J]. Remote Sensing for Land & Resources , 2019 , 31 ( 1 ): 1 - 7 .
HUSSAIN M , CHEN D M , CHENG A , et al . Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2013 , 80 : 91 - 106 .
ZHANG M , HU X Y , ZHAO L K , et al . Learning Dual Multi-scale Manifold Ranking for Semantic Segmentation of High-Rresolution Images [J]. Remote Sensing , 2017 , 9 ( 5 ): 500 .
ZHANG J M , HU X Y , WEI Y J , et al . Road Topology Extraction from Satellite Imagery by Joint Learning of Nodes and Their Connectivity [J]. IEEE Transactions on Geoscience and Remote Sensing , 2023 , 61 : 5602613 .
YANG B N , ZHANG M , ZHANG Z , et al . TopDiG: Class-Agnostic Topological Directional Graph Extraction from Remote Sensing Images [C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Vancouver, BC, Canada,2023 .
SHU Z , HU X Y , SUN J . Center-Point-Guided Proposal Generation for Detection of Small and Dense Buildings in Aerial Imagery [J]. IEEE Geoscience and Remote Sensing Letters , 2018 , 15 ( 7 ): 1100 - 1104 .
胡翔云 , 巩晓雅 , 张觅 . 变分法遥感影像人工地物自动检测 [J]. 测绘学报 , 2018 , 47 ( 6 ): 780 - 789 .
HU Xiangyun , GONG Xiaoya , ZHANG Mi . A Variational Approach for Automatic Man-Made Object Detection from Remote Sensing Images [J]. Acta Geodaetica et Cartographica Sinica , 2018 , 47 ( 6 ): 780 - 789 .
WU T , HU X Y , ZHANG Y , et al . Automatic Cloud Detection for High Resolution Satellite Stereo Images and Its Application in Terrain Extraction [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2016 , 121 : 143 - 156 .
眭海刚 , 冯文卿 , 李文卓 , 等 . 多时相遥感影像变化检测方法综述 [J]. 武汉大学学报(信息科学版) , 2018 , 43 ( 12 ): 1885 - 1898 .
SUI Haigang , FENG Wenqing , LI Wenzhuo , et al . Review of Change Detection Methods for Multi-temporal Remote Sensing Imagery [J]. Geomatics and Information Science of Wuhan University , 2018 , 43 ( 12 ): 1885 - 1898 .
JIANG H W , HU X Y , LI K , et al . PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection [J]. Remote Sensing , 2020 , 12 ( 3 ): 484 .
ZHANG M , HU X Y . Translation-Aware Semantic Segmentation via Conditional Least-Square Generative Adversarial Networks [J]. Journal of Applied Remote Sensing , 2017 , 11 ( 4 ): 1 .
GONG J Q , HU X Y , PANG S Y , et al . Roof-Cut Guided Localization for Building Change Detection from Imagery and Footprint Map [J]. Photogrammetric Engineering & Remote Sensing , 2019 , 85 ( 8 ): 543 - 558 .
GONG J Q , HU X Y , PANG S Y , et al . Patch Matching and Dense CRF-Based Co-refinement for Building Change Detection from Bi-temporal Aerial Images [J]. Sensors , 2019 , 19 ( 7 ): 1557 .
PANG S Y , HU X Y , ZHANG M , et al . Co-segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-temporal Digital Surface Models and Aerial Images [J]. Remote Sensing , 2019 , 11 ( 6 ): 729 .
ZHANG L , HU X Y , ZHANG M , et al . Object-Level Change Detection with a Dual Correlation Attention-Guided Detector [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2021 , 177 : 147 - 160 .
乔俊平 , 温建兵 , 吴满意 , 等 . EasyFeature在影像特征智能提取中的效率测试 [J]. 测绘标准化 , 2017 , 33 ( 1 ): 13 - 14 .
QIAO Junping , WEN Jianbing , WU Manyi , et al . Efficiency Tests of EasyFeature in Image Features Extraction [J]. Standardization of Surveying and Mapping , 2017 , 33 ( 1 ): 13 - 14 .
LI K , HU X Y , JIANG H W , et al . Attention-Guided Multi-scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery [J]. Remote Sensing , 2020 , 12 ( 5 ): 789 .
李小凯 . 高分辨率遥感影像面状地物交互式提取方法研究 [D]. 武汉 : 武汉大学 , 2016 .
LI Xiaokai . Study on Interactive Extraction Method of Area Objects from High Resolution Remote Sensing Images [D]. Wuhan : Wuhan University , 2016 .
KIRILLOV A , MINTUN E , RAVI N , et al . Segment Anything [EB/OL]. arXiv : 2304 . 02643 , https://arxiv.org/abs/2304.02643v1 https://arxiv.org/abs/2304.02643v1 , 2023 .
YIN S K , FU C Y , ZHAO S R , et al . A Survey on Multimodal Large Language Models [EB/OL]. arXiv : 2306 . 13549 , https://arxiv.org/abs/2306.13549v2 https://arxiv.org/abs/2306.13549v2 , 2023 .
ZHANG C H , LIU L , CUI Y W , et al . A Comprehensive Survey on Segment Anything Model for Vision and Beyond [EB/OL]. arXiv : 2305 . 08196 , https://arxiv.org/abs/2305.08196v2 https://arxiv.org/abs/2305.08196v2 , 2023 .
0
浏览量
0
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621