English

应用科学学报 >

2022 , Vol. 40 >Issue 3: 372 - 388

DOI: https://doi.org/10.3969/j.issn.0255-8297.2022.03.002

信号与信息处理

高分遥感影像不同形状建筑物半自动提取与规则化

展开
  • 1. 武汉大学 遥感信息工程学院, 湖北 武汉 430079;
    2. 中国电子科技集团公司 航天信息应用技术重点实验室, 河北 石家庄 050081

收稿日期: 2021-02-01

  网络出版日期: 2022-05-25

基金资助

国家自然科学基金(No.U2033216)资助

收起

Semi-automatic Extraction and Regularization of Buildings of Different Shapes from High-Resolution Remote Sensing Images

Expand
  • 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China;
    2. CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, Hebei, China

Received date: 2021-02-01

  Online published: 2022-05-25

Fold

摘要

现有的高空间分辨率遥感影像交互式建筑物提取方法需要用户在建筑物上勾画出与建筑物大小和形状相近的线，且大多方法只能提取直角建筑物。为降低交互要求并实现不同形状建筑物的精确提取，该文首先在用户少量交互的基础上采用区域生长、高斯混合模型、CannyLines线段检测算法以及基于多星形约束的最大流/最小割分割模型获得建筑物图斑，然后分别针对直角建筑物和非直角建筑物图斑进行规则化，得到与实际建筑物形状一致的提取结果。实验表明，该方法交互简单且建筑物提取精度F1值可达到0.9，具有较强的鲁棒性。

关键词： 高分遥感影像; 半自动; 建筑物提取; 规则化

本文引用格式

崔卫红, 李佳, 刘宇 . 高分遥感影像不同形状建筑物半自动提取与规则化[J]. 应用科学学报, 2022 , 40(3) : 372 -388 . DOI: 10.3969/j.issn.0255-8297.2022.03.002

Abstract

The current methods of interactive extraction of buildings from high-resolution remote sensing images mostly require complex user interaction and most of them only support extraction of buildings with right angles. In order to reduce interaction and achieve high-precision extraction of buildings in different shapes, this paper uses region grow, Gaussian mixture models (GMM), CannyLines edge detection and the max-flow/min-cut segmentation method based on multiple star constraints sequentially to obtain building patch, followed by regularization methods to get the building contours which are consistent with the actual building shapes. The average of F1 is up to 0.9 in extraction experiments, and the experimental results also show the facility and strong robustness of the proposed method.

Key words： high-resolution remote sensing images; semi-automatic; building extraction; regularization

参考文献

[1] Noronha S, Nevatia R. Detection and description of buildings from multiple aerial images[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997:588-594.
[2] Brunn A, Weidner U. Extracting buildings from digital surface models[J]. International Archives of Photogrammetry and Remote Sensing, 1997, 32(3):27-34.
[3] Izadi M P. Three-dimensional polygonal building model estimation from single satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(6):2254-2272.
[4] Jin X Y, Davis C H. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information[J]. Eurasip Journal on Advances in Signal Processing, 2005(14):2196-2206.
[5] Sirmacek B, Unsalan C. A probabilistic framework to detect buildings in aerial and satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1):211-221.
[6] Akcay H G, Aksoy S. Building detection using directional spatial constraints[C]//2010 IEEE International Geoscience and Remote Sensing Symposium, 2010:1932-1935.
[7] Wang J, Yang X C, Qin X, et al. An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3):487-491.
[8] Ngo T T, Collet C, Mazet V. Automatic rectangular building detection from VHR aerial imagery using shadow and image segmentation[C]//2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015:1483-1487.
[9] Ok A O, Senaras C, Yuksel B. Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3):1701-1717.
[10] Huang X, Zhang L. A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery[J]. Photogrammetric Engineering&Remote Sensing, 2011, 77(7):721-732.
[11] Huang X, Zhang L. Morphological building shadow index for building extraction from highresolution imagery over urban areas[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1):161-172.
[12] Huang X, Yuan W, Li J, et al. A new building extraction post-processing framework for high-spatial-resolution remote-sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(2):654-668.
[13] Xu Y, Wu L, Xie Z, et al. Building extraction in very high resolution remote sensing imagery using deep learning and guided filters[J]. Remote Sensing, 2018, 10(1):144.
[14] Sun Y, Zhang X, Zhao X, et al. Extracting building boundaries from high resolution optical images and LiDAR data by integrating the convolutional neural network and the active contour model[J]. Remote Sensing, 2018, 10(9):1459.
[15] Bischke B, Helber P, Folz J, et al. Multi-task learning for segmentation of building footprints with deep neural networks[C]//2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019:1480-1484.
[16] Mayunga S D, Coleman D J, Zhang Y. Semi-automatic building extraction in dense urban settlement areas from high-resolution satellite images[J]. Empire Survey Review, 2010, 42(315):50-61.
[17] Jiang N, Zhang J X, Li H T, et al. Semi-automatic building extraction from high resolution imagery based on segmentation[C]//2008 International Workshop on Earth Observation and Remote Sensing Applications. IEEE, 2008:1-5.
[18] Fazan A J, Poz A P D. Rectilinear building roof contour extraction based on snakes and dynamic programming[J]. International Journal of Applied Earth Observation&Geoinformation, 2013, 25:1-10.
[19] Tan Y, Yu Y, Xiong S, et al. Semi-automatic building extraction from very high resolution remote sensing imagery via energy minimization model[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2016:657-660.
[20] 丁亚洲,冯发杰,吏军平,等.多星形约束图割与轮廓规则化的高分遥感影像直角建筑物提取[J].测绘学报, 2018, 47(12):1630-1639. Ding Y Z, Feng F J, Li J P, et al. Right-angle buildings extraction from high-resolution aerial image based on multi-stars constraint segmentation and regularization[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12):1630-1639.(in Chinese)
[21] 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.
[22] Das P, Veksler O, Zavadsky V, et al. Semiautomatic segmentation with compact shape prior[J]. Image and Vision Computing, 2009, 27(1/2):206-219.
[23] Lu X, Yao J, Li K, et al. CannyLines:a parameter-free line segment detector[C]//2015 IEEE International Conference on Image Processing (ICIP), 2015:507-511.
[24] Gulshan V, Rother C, Criminisi A, et al. Geodesic star convexity for interactive image segmentation[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010:3129-3136.
[25] He K, Sun J, Tang X. Guided image filtering[C]//European Conference on Computer Vision. Berlin, Heidelberg:Springer, 2010:1-14.
[26] 余毓杰.高分遥感影像建筑物半自动提取算法研究[D].武汉:华中科技大学, 2016.
[27] Smith C. A characterization of star-shaped sets[J]. The American Mathematical Monthly, 1968, 75(4):386.
[28] Toranzos F A, Cunto A F. Sets expressible as finite unions of starshaped sets[J]. Journal of Geometry, 2004, 79(1):190-195.
[29] Boykov Y, Kolmogorov V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9):359-374.
[30] Farin G. Algorithms for rational Bézier curves[J]. Computer-Aided Design, 1983, 15(2):73-77.
[31] Fitzgibbon A, Pilu M, Fisher R B. Direct least square fitting of ellipses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5):476-480.
[32] Hu M. Visual pattern recognition by moment invariants[J]. IRE Transactions on Information Theory, 1962, 8(2):179-187.
[33] 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]. The Canadian Geogra, 1973, 10(2):112-122.
[34] Gribov A, Bodansky E. A new method of polyline approximation[C]//Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Berlin, Heidelberg:Springer, 2004:504-511.
[35] 曾静静,卢秀山,王健,等. LiDAR数据不规则建筑物的矢量化[J].测绘工程, 2011, 20(4):60-62. Zeng J J, Lu X S, Wang J, et al. LiDAR data vector of irregular buildings[J]. Engineering of Surveying&Mapping, 2011, 20(4):60-62.(in Chinese)
[36] Gribov A, Bodansky E. Reconstruction of orthogonal polygonal lines[C]//International Workshop on Document Analysis Systems. Berlin, Heidelberg:Springer, 2006:462-473.
Options
文章导航

/

〈 〉