基于多尺度多特征的高空间分辨率遥感影像建筑物自动化检测
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摘要
建筑物检测在城市规划、变化检测、地表覆盖等方面均起到重要作用。然而高空间分辨率遥感影像(简称"高分影像")中建筑物朝向不一,形态颜色各异,大小尺寸也有着较大差别,使得建筑物检测成为一道难题。为此,提出一种基于多尺度多特征来自动化检测高分影像中建筑物的方法:首先,对影像降采样构建高斯金字塔模型,固定尺度的滑动窗口在不同层影像中对应着不同的实际地面面积;然后,对影像进行超像素分割并计算滑动窗口中多种描述建筑物特性的特征值,通过多特征融合来衡量建筑物目标在不同尺度影像中的显著性;最后,计算超像素块的显著性均值,结合Otsu算法自动求取阈值,进一步设置长宽比等约束条件,从而准确、自动地提取建筑物目标。分别采用空间分辨率为0. 5 m和0. 2 m的影像进行实验,并和基于颜色和纹理建模的马尔科夫随机场模型算法进行定性和定量的比较。实验结果表明,该方法对高分影像中建筑物的提取有更好的实际效果和检测精度。
Building detection plays an important role in urban planning,change detection,surface coverage and so on.However,in high resolution remote sensing images,buildings vary in shape,color,and size,which makes building detection a difficult problem.Therefore,this paper proposes a method based on multi-scale and multi-feature to automatically extract buildings in high resolution images:Firstly,down sampling images are used to construct Gauss pyramid model,while fixed size windows in different layers of pyramid image represent different ground areas.Then multi features are calculated which describe building characteristics by sliding windows,and multi features are fused to evaluate the saliency of building in different scales.Then the saliency of superpixels is calculated,and Otsu algorithm is used to automatically determine the threshold,and furthermore,some constraints such as the aspect ratio were combined to extract buildings accurately and automatically.Experiments were made by0.5 m and 0.2 m high resolution remote sensing images in comparison with the markov random field model based on color and texture modeling algorithm for qualitative and quantitative comparison.The results show that the method suggested in this paper can obtain more satisfactory precision and has higher effect on building detection from high-resolution remote sensing images.
Building detection plays an important role in urban planning,change detection,surface coverage and so on.However,in high resolution remote sensing images,buildings vary in shape,color,and size,which makes building detection a difficult problem.Therefore,this paper proposes a method based on multi-scale and multi-feature to automatically extract buildings in high resolution images:Firstly,down sampling images are used to construct Gauss pyramid model,while fixed size windows in different layers of pyramid image represent different ground areas.Then multi features are calculated which describe building characteristics by sliding windows,and multi features are fused to evaluate the saliency of building in different scales.Then the saliency of superpixels is calculated,and Otsu algorithm is used to automatically determine the threshold,and furthermore,some constraints such as the aspect ratio were combined to extract buildings accurately and automatically.Experiments were made by0.5 m and 0.2 m high resolution remote sensing images in comparison with the markov random field model based on color and texture modeling algorithm for qualitative and quantitative comparison.The results show that the method suggested in this paper can obtain more satisfactory precision and has higher effect on building detection from high-resolution remote sensing images.
引文
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[3]吕凤华,舒宁,龚龑,等.利用多特征进行航空影像建筑物提取[J].武汉大学学报(信息科学版),2017,42(5):656-660.Lyu F H,Shu N,Gong Y,et al.Regular building extraction from high resolution image based on multilevel-features[J].Geomatics and Information Science of Wuhan University,2017,42(5):656-660.
[4]谭衢霖.高分辨率多光谱影像城区建筑物提取研究[J].测绘学报,2010,39(6):618-623.Tan Q L.Urban building extraction from VHR multi-spectral images using object-based classification[J].Acta Geodaetica et Cartographica Sinica,2010,39(6):618-623.
[5]徐宏根,宋妍.顾及阴影信息的高分辨率遥感图像变化检测方法[J].国土资源遥感,2013,25(4):16-21.doi:10.6046/gtzyyg.2013.04.03.Xu H G,Song Y.Change detection method taking into account shadow information for high resolution remote sensing image[J].Remote Sensing for Land and Resources,2013,25(4):16-21.doi:10.6046/gtzyyg.2013.04.03.
[6]庞池海,李光耀,赵洁,等.基于直线检测算法的卫星图片中建筑物轮廓提取[J].计算机应用,2008,28(s1):190-192.Pang C H,Li G Y,Zhao J,et al.Building figure extraction in satellite images based on line detection algorithm[J].Computer Applications,2008,28(s1):190-192.
[7]赵传,张保明,陈小卫,等.一种基于Li DAR点云的建筑物提取方法[J].测绘通报,2017,(2):35-39.Zhao C,Zhang B M,Chen X W,et al.A method of extracting building based on Li DAR point clouds[J].Bulletin of Surveying and Mapping,2017,(2):35-39.
[8]王雪,李培军,姜莎莎,等.利用机载Li DAR数据和高分辨率图像提取复杂城区建筑物[J].国土资源遥感,2016,28(2):106-111.doi:10.6046/gtzyyg.2016.02.17.Wang X,Li P J,Jiang S S,et al.Building extraction using airborne Li DAR data and very high resolution imagery over a complex urban area[J].Remote Sensing for Land and Resources,2016,28(2):106-111.doi:10.6046/gtzyyg.2016.02.17.
[9]Vakalopoulou M,Karantzalos K,Komodakis N,et al.Building detection in very high resolution multispectral data with deep learning features[C]//Geoscience and Remote Sensing Symposium,IEEE,2015:1873-1876.
[10]陈文康.基于深度学习的农村建筑物遥感影像检测[J].测绘,2016,39(5):227-230.Chen W K.Remote sensing image detection of rural buildings based on deep learning algorithm[J].Surveying and Mapping,2016,39(5):227-230.
[11]Wang H Y,Pan D L,Xia D S.Fast algorithm for two-dimensional Otsu adaptive threshold algorithm[J].Journal of Image and Graphics,2005,33(9):968-971.
[12]Hu X Y,Shen J J,Shan J,et al.Local edge distributions for detection of salient structure textures and objects[J].IEEE Geoscience and Remote Sensing Letters,2013,10(3):466-470.
[13]Alexe B,Deselaers T,Ferrari V.Measuring the objectness of image windows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2189-2202.
[14]Cheng Y Z.Mean Shift,mode seeking,and clustering[J].IEEETransactions on Pattern Analysis and Machine Intelligence,1995,17(8):790-799.
[15]Gu W,Lyu Z H,Hao M.Change Detection Method for Remote Sensing Images Based on An Improved Markov Random Field[M].The Netherlands:Kluwer Academic Publishers,2017.