测绘学报 ›› 2024, Vol. 53 ›› Issue (7): 1444-1457.doi: 10.11947/j.AGCS.2024.20230056
殷吉崇(), 武芳(), 翟仁健, 邱越, 巩现勇, 行瑞星
收稿日期:
2023-03-20
发布日期:
2024-08-12
通讯作者:
武芳
E-mail:jichongy@whu.edu.cn;wufang_630@126.com
作者简介:
殷吉崇(1997—),男,博士生,研究方向为地理空间数据智能处理。E-mail:jichongy@whu.edu.cn
基金资助:
Jichong YIN(), Fang WU(), Renjian ZHAI, Yue QIU, Xianyong GONG, Ruixing XING
Received:
2023-03-20
Published:
2024-08-12
Contact:
Fang WU
E-mail:jichongy@whu.edu.cn;wufang_630@126.com
About author:
YIN Jichong (1997—), male, PhD candidate, majors in intelligent processing of geospatial data. E-mail: jichongy@whu.edu.cn
Supported by:
摘要:
高分辨率遥感影像建筑物提取目前仍是遥感应用与地图制图领域的研究热点和难点。尽管深度学习方法的引入极大地提升了建筑物分割的精度,但建筑物分割掩膜中轮廓不规则和边界不清晰的问题依然存在。为了获取规则的建筑物轮廓和清晰的边界,本文基于双路径边界约束与相对论生成对抗网络提出一种建筑物轮廓规则化方法。该网络由双路径边界约束生成器和相对论平均鉴别器共同组成。双路径边界约束生成器通过双路径网络架构和边界损失函数来融合遥感影像和输入标签的边界细节信息来,从而生成规则的建筑物轮廓;而相对论平均鉴别器则通过评估地面实况标签与生成的规则化结果之间的质量差异来迫使生成器生成更为真实的建筑物掩膜。为验证模型性能、探索性能提升原因,本文在WHU建筑物数据集和Inria航空影像标注数据集上设计了对比试验和消融试验。试验结果表明,本文方法可以生成吻合地面实况标签的规则化结果,在解决分割掩膜边界模糊、轮廓不规则的问题上具有显著优势。
中图分类号:
殷吉崇, 武芳, 翟仁健, 邱越, 巩现勇, 行瑞星. 面向建筑物轮廓规则化的双路径边界约束与相对论生成对抗网络[J]. 测绘学报, 2024, 53(7): 1444-1457.
Jichong YIN, Fang WU, Renjian ZHAI, Yue QIU, Xianyong GONG, Ruixing XING. Two-stream boundary constraints and relativistic generation adversarial network for building contour regularization[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1444-1457.
[1] | 龚健雅, 季顺平. 摄影测量与深度学习[J]. 测绘学报, 2018, 47(6):693-704. DOI: 10.11947/j.AGCS.2018.20170640. |
GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):693-704. DOI: 10.11947/j.AGCS.2018.20170640. | |
[2] | 龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报(信息科学版), 2018, 43(12):1788-1796. |
GONG Jianya. Chances and challenges for development of surveying and remote sensing in the age of artificial intelligence[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1788-1796. | |
[3] | ALSHEHHI R, MARPU P R, WOON W L, et al. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:139-149. |
[4] | DORNAIKA F, MOUJAHID A, EL MERABET Y, et al. Building detection from orthophotos using a machine learning approach: an empirical study on image segmentation and descriptors[J]. Expert Systems with Applications, 2016, 58:130-142. |
[5] | CHEN Z, LI S, XU Y, et al. Correg-YOLOV3: a method for dense buildings detection in high-resolution remote sensing images[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(2):51-61. |
[6] | JI Shunping, WEI Shiqing, LU Meng. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1):574-586. |
[7] | 张玉鑫, 颜青松, 邓非. 高分辨率遥感影像建筑物提取多路径RSU网络法[J]. 测绘学报, 2022, 51(1):135-144. DOI: 10.11947/j.AGCS.2021.20200508. |
ZHANG Yuxin, YAN Qingsong, DENG Fei. Multi-path RSU network method for high-resolution remote sensing image building extraction[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1):135-144. DOI: 10.11947/j.AGCS.2021.20200508. | |
[8] | 季顺平, 魏世清. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J]. 测绘学报, 2019, 48(4):448-459. DOI: 10.11947/j.AGCS.2019.20180206. |
JI Shunping, WEI Shiqing. Building extraction via convolutional neural networks from an open remote sensing building dataset[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4):448-459. DOI: 10.11947/j.AGCS.2019.20180206. | |
[9] | YUAN Jiangye. Learning building extraction in aerial scenes with convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(11):2793-2798. |
[10] | MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. Convolutional neural networks for large-scale remote-sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):645-657. |
[11] | KANG Wenchao, XIANG Yuming, WANG Feng, et al. EU-net: an efficient fully convolutional network for building extraction from optical remote sensing images[J]. Remote Sensing, 2019, 11(23):2813. |
[12] | 何直蒙, 丁海勇, 安炳琪. 高分辨率遥感影像建筑物提取的空洞卷积E-Unet算法[J]. 测绘学报, 2022, 51(3):457-467. DOI: 10.11947/j.AGCS.2022.20200601. |
HE Zhimeng, DING Haiyong, AN Bingqi. E-Unet: a atrous convolution-based neural network for building extraction from high-resolution remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(3):457-467. DOI: 10.11947/j.AGCS.2022.20200601. | |
[13] | QIU Yue, WU Fang, QIAN Haizhong, et al. AFL-net: attentional feature learning network for building extraction from remote sensing images[J]. Remote Sensing, 2022, 15(1):95. |
[14] | CHEN J, LI Z, LI S, et al. From digitalized to intelligentized surveying and mapping: fundamental issues and research agenda[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2):148-160. |
[15] | ZHENG X, HUAN L, XIA G S, et al. Parsing very high-resolution urban scene images by learning deep ConvNets with edge-aware loss[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170:15-28. |
[16] | YIN Jichong, WU Fang, QIU Yue, et al. A multiscale and multitask deep learning framework for automatic building extraction[J]. Remote Sensing, 2022, 14(19):4744. |
[17] | GUO Haonan, DU Bo, ZHANG Liangpei, et al. A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 183:240-252. |
[18] | LI Qingyu, SHI Yilei, HUANG Xin, et al. Building footprint generation by integrating convolution neural network with feature pairwise conditional random field (FPCRF)[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(11):7502-7519. |
[19] | ZHAO K, KANG J, JUNG J, et al. Building extraction from satellite images using mask R-CNN with building boundary regularization[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 247-251. |
[20] | HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2):386-397. |
[21] | WEN Qi, JIANG Kaiyu, WANG Wei, et al. Automatic building extraction from google earth images under complex backgrounds based on deep instance segmentation network[J]. Sensors, 2019, 19(2):333. |
[22] | 朱盼盼, 李帅朋, 张立强, 等. 基于多任务学习的高分辨率遥感影像建筑提取[J]. 地球信息科学学报, 2021, 23(3):514-523. |
ZHU Panpan, LI Shuaipeng, ZHANG Liqiang, et al. Multitask learning-based building extraction from high-resolution remote sensing images[J]. Journal of Geo-Information Science, 2021, 23(3):514-523. | |
[23] | RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241. |
[24] | DING Lei, TANG Hao, LIU Yahui, et al. Adversarial shape learning for building extraction in VHR remote sensing images[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2022, 31:678-690. |
[25] | GUO Haonan, SHI Qian, MARINONI A, et al. Deep building footprint update network: a semi-supervised method for updating existing building footprint from bi-temporal remote sensing images[J]. Remote Sensing of Environment, 2021, 264:112589. |
[26] | WANG Zhenqing, ZHOU Yi, WANG Futao, et al. A multi-scale edge constraint network for the fine extraction of buildings from remote sensing images[J]. Remote Sensing, 2023, 15(4):927. |
[27] | DOUGLAS D H, PEUCKER T K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature[J]. Cartographica: the International Journal for Geographic Information and Geovisualization, 1973, 10(2):112-122. |
[28] | TASAR O, MAGGIORI E, ALLIEZ P, et al. Polygonization of binary classification maps using mesh approximation with right angle regularity[C]//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE, 2018: 6404-6407. |
[29] | MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. Polygonization of remote sensing classification maps by mesh approximation[C]//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing: IEEE, 2017: 560-564. |
[30] | GIRARD N, TARABALKA Y. End-to-end learning of polygons for remote sensing image classification[C]//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE, 2018: 2083-2086. |
[31] | WEI Shiqing, JI Shunping, LU Meng. Toward automatic building footprint delineation from aerial images using CNN and regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3):2178-2189. |
[32] | ZORZI S, FRAUNDORFER F. Regularization of building boundaries in satellite images using adversarial and regularized losses[C]//Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE, 2019: 5140-5143. |
[33] | LI M, LAFARGE F, MARLET R. Approximating shapes in images with low-complexity polygons[C]//Proceedings of 2020 IEEE Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 8630-8638. |
[34] | GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing System. Cambridge: MIT Press, 2014: 2672-2680. |
[35] | LIU Q, MENG X, SHAO F, et al. PSTAF-GAN: progressive spatio-temporal attention fusion method based on generative adversarial network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:5408513. |
[36] | BENZENATI T, KESSENTINI Y, KALLEL A. Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks[J]. Expert Systems with Applications, 2022, 188:115996. |
[37] | TANG Meng, PERAZZI F, DJELOUAH A, et al. On regularized losses for weakly-supervised CNN segmentation[C]//Proceedings of 2018 European Conference on Computer Vision. Munich: Springer, 2018: 507-522. |
[38] | TANG Meng, DJELOUAH A, PERAZZI F, et al. Normalized cut loss for weakly-supervised CNN segmentation[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1818-1827. |
[39] | CHEN Zhao, BADRINARAYANAN V, LEE Chenyu, et al. GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks[C]//Proceedings of the 35th International Conference on Machine Learning. Stockholm: PMLR, 2018: 794-803. |
[40] | MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. Can semantic labeling methods generalize to any city?The Inria aerial image labeling benchmark[C]//Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE, 2017: 3226-3229. |
[41] | XIE Yakun, ZHU Jun, CAO Yungang, et al. Refined extraction of building outlines from high-resolution remote sensing imagery based on a multifeature convolutional neural network and morphological filtering[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:1842-1855. |
[42] | YE Su, PONTIUS R G J, RAKSHIT R. A review of accuracy assessment for object-based image analysis: from per-pixel to per-polygon approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 141:137-147. |
[43] | PERSELLO C, BRUZZONE L. A novel protocol for accuracy assessment in classification of very high resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3):1232-1244. |
[44] | LIZARAZO I. Accuracy assessment of object-based image classification: another STEP[J]. International Journal of Remote Sensing, 2014, 35(16):6135-6156. |
[1] | 顾海燕, 杨懿, 李海涛, 孙立坚, 丁少鹏, 刘世琦. 高分辨率遥感影像样本库动态构建与智能解译应用[J]. 测绘学报, 2024, 53(6): 1165-1179. |
[2] | 彭代锋, 翟晨晨, 周顶蔚, 张永军, 管海燕, 臧玉府. 基于金字塔语义token全局信息增强的高分光学遥感影像变化检测[J]. 测绘学报, 2024, 53(6): 1195-1211. |
[3] | 王继成, 郭安嵋, 慎利, 蓝天, 徐柱, 李志林. 多级对比学习下的弱监督高分遥感影像城市固废堆场提取[J]. 测绘学报, 2024, 53(6): 1212-1223. |
[4] | 林云浩, 王艳军, 李少春, 蔡恒藩. 一种耦合DeepLab与Transformer的农作物种植类型遥感精细分类方法[J]. 测绘学报, 2024, 53(2): 353-366. |
[5] | 胡明洪, 李佳田, 姚彦吉, 阿晓荟, 陆美, 李文. 结合多路径的高分辨率遥感影像建筑物提取SER-UNet算法[J]. 测绘学报, 2023, 52(5): 808-817. |
[6] | 胡安娜, 刘睿, 吴亮, 张进, 徐永洋, 陈思琼. 顾及全局特征和纹理特征的遥感影像超分辨率重建方法[J]. 测绘学报, 2023, 52(4): 648-659. |
[7] | 姜明, 张新长, 孙颖, 冯炜明, 阮永俭. 全尺度特征聚合的高分辨率遥感影像变化检测网络[J]. 测绘学报, 2023, 52(10): 1738-1748. |
[8] | 张广斌, 高贤君, 冉树浩, 杨元维, 李丽珊, 张妍. 高分遥感影像云雪共存区轻量云高精度检测方法[J]. 测绘学报, 2023, 52(1): 93-107. |
[9] | 何直蒙, 丁海勇, 安炳琪. 高分辨率遥感影像建筑物提取的空洞卷积E-Unet算法[J]. 测绘学报, 2022, 51(3): 457-467. |
[10] | 葛小三, 陈曦, 赵文智, 李瑞祥. 基于生成对抗网络的建筑物损毁检测[J]. 测绘学报, 2022, 51(2): 238-247. |
[11] | 张玉鑫, 颜青松, 邓非. 高分辨率遥感影像建筑物提取多路径RSU网络法[J]. 测绘学报, 2022, 51(1): 135-144. |
[12] | 郑凯, 李建胜, 王俊强, 欧阳文, 谷友艺, 张迅. DCLS-GAN:利用生成对抗网络的天绘一号卫星高原地区影像去云方法[J]. 测绘学报, 2021, 50(2): 248-259. |
[13] | 张涛, 丁乐乐, 史芙蓉. 高分辨率遥感影像城中村提取的景观语义指数方法[J]. 测绘学报, 2021, 50(1): 97-104. |
[14] | 王舒洋, 慕晓冬, 贺浩, 杨东方, 马晨晖. 航拍图像跨数据域特征迁移道路提取方法[J]. 测绘学报, 2020, 49(5): 611-621. |
[15] | 李雪, 张力, 王庆栋, 艾海滨. 多时相遥感影像语义分割色彩一致性对抗网络[J]. 测绘学报, 2020, 49(11): 1473-1484. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||