高分辨率遥感影像建筑物分级提取
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摘要
高分辨率遥感影像建筑物信息自动提取是遥感应用研究中的一个热点问题,但由于受到成像条件不同、背景地物复杂、建筑物类型多样等多个因素的影响使得建筑物的自动提取仍然十分困难。为此,在综合考虑影像光谱、几何与上下文特征的基础上,提出了一种基于面向对象与形态学相结合的高分辨率遥感影像建筑物信息分级提取方法。该方法首先利用影像的多尺度及多方向Gabor小波变换结果提取建筑物特征点;然后采用面向对象的思想构建空间投票矩阵来度量每一个像素点属于建筑物区域的概率,从而提取出建筑物区域边界;最后在提取的建筑物区域内应用形态学建筑物指数实现建筑物信息的自动提取。实验结果表明,本文方法能够高效、高精度地完成复杂场景下的建筑物信息提取,且提取结果的正确性和完整性都优于效果较好的PanTex算法。
The precise location and identification of buildings are of importance to many geospatial applications. High-resolution satellite images with multispectral channels contain abundant spectral and structural information about ground objects, making these images more suitable for automatic building detection. However, the automatic detection of buildings is still very difficult owing to many obstacles, such as different imaging conditions, complex background, and various types of buildings. Therefore, this paper proposes a novel hierarchical building extraction method based on object-oriented and morphological models for automatic building detection from high-resolution satellite images captured in complex scenes. The proposed method first extracts build-up areas from high-resolution satellite images and then detects buildings from the extracted build-up areas. In the procedure of build-up areas extraction, the multi-scale and multi-directional Gabor wavelet transform is first applied to high-resolution satellite images. Then, a scale-invariant feature point detection algorithm that considers the multi-scale and multi-directional texture properties of build-up areas is proposed for the detection of building feature points. Subsequently, watershed segmentation algorithm with threshold mark is utilized to obtain homogeneous regions, and a spatial voting matrix is computed based on these homogeneous regions and the detected feature points to obtain confidence map. Finally, build-up areas are extracted by segmenting the confidence map using adaptive thresholding algorithm. In the procedure of building extraction, the morphological building index(MBI) is first applied to the extracted build-up areas, and then the initial building results are obtained by performing threshold segmentation on the MBI feature image.Finally, shape attributes such as length–width ratio are used to further refine the initial building extraction results. The performance of the proposed method is evaluated using three high-resolution satellite images captured in complex environments.Evaluation results show that the proposed method can efficiently and accurately detect buildings in complex scenes with an overall accuracy and Kappa coefficient greater than 90% and 0.8, respectively. The proposed method also improves the omission and commission errors by10.03% and 6.86% on average, respectively, as compared with the performance of the PanTex algorithm. A novel hierarchical building extraction method based on object-oriented and morphological models is proposed in this study. The experimental results highlight the advantages of the hierarchical extraction strategy and demonstrate that the proposed method outperforms the PanTex algorithm. However, good performance of the proposed method relies heavily on the detection of built-up areas, and further improvements should be performed in the future.
The precise location and identification of buildings are of importance to many geospatial applications. High-resolution satellite images with multispectral channels contain abundant spectral and structural information about ground objects, making these images more suitable for automatic building detection. However, the automatic detection of buildings is still very difficult owing to many obstacles, such as different imaging conditions, complex background, and various types of buildings. Therefore, this paper proposes a novel hierarchical building extraction method based on object-oriented and morphological models for automatic building detection from high-resolution satellite images captured in complex scenes. The proposed method first extracts build-up areas from high-resolution satellite images and then detects buildings from the extracted build-up areas. In the procedure of build-up areas extraction, the multi-scale and multi-directional Gabor wavelet transform is first applied to high-resolution satellite images. Then, a scale-invariant feature point detection algorithm that considers the multi-scale and multi-directional texture properties of build-up areas is proposed for the detection of building feature points. Subsequently, watershed segmentation algorithm with threshold mark is utilized to obtain homogeneous regions, and a spatial voting matrix is computed based on these homogeneous regions and the detected feature points to obtain confidence map. Finally, build-up areas are extracted by segmenting the confidence map using adaptive thresholding algorithm. In the procedure of building extraction, the morphological building index(MBI) is first applied to the extracted build-up areas, and then the initial building results are obtained by performing threshold segmentation on the MBI feature image.Finally, shape attributes such as length–width ratio are used to further refine the initial building extraction results. The performance of the proposed method is evaluated using three high-resolution satellite images captured in complex environments.Evaluation results show that the proposed method can efficiently and accurately detect buildings in complex scenes with an overall accuracy and Kappa coefficient greater than 90% and 0.8, respectively. The proposed method also improves the omission and commission errors by10.03% and 6.86% on average, respectively, as compared with the performance of the PanTex algorithm. A novel hierarchical building extraction method based on object-oriented and morphological models is proposed in this study. The experimental results highlight the advantages of the hierarchical extraction strategy and demonstrate that the proposed method outperforms the PanTex algorithm. However, good performance of the proposed method relies heavily on the detection of built-up areas, and further improvements should be performed in the future.
引文
Alshehhi R,Marpu P R,Woon L W and Mura M D.2017.Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks.ISPRS Journal of Photogrammetry and Remote Sensing,130:139-149[DOI:10.1016/j.isprsjprs.2017.05.002]
Guo Q S,Wei Z W,Wang Y and Wang L.2017.The method of extracting spatial distribution characteristics of buildings combined with feature classification and proximity graph.Acta Geodaetica et Cartographica Sinica,46(5):631-638(郭庆胜,魏智威,王勇,王琳.2017.特征分类与邻近图相结合的建筑物群空间分布特征提取方法.测绘学报,46(5):631-638)[DOI:10.11947/j.AGCS.2017.20160374]
Hou Y,Zhou S L,Lei L and Zhao J.2013.Invariant feature with multicharacteristic scales using Gabor filter bank.Acta Electronica Sinica,41(6):1146-1152(侯毅,周石琳,雷琳,赵键.2013.基于Gabor滤波器组的多特征尺度不变特征提取方法.电子学报,41(6):1146-1152)[DOI:10.3969/j.issn.0372-2112.2013.06.016]
Hu X Y,Shen J J,Shan J and Pan L.2013.Local edge distributions for detection of salient structure textures and objects.IEEEGeoscience and Remote Sensing Letters,10(3):466-470[DOI:10.1109/LGRS.2012.2210188]
Huang X and Zhang L P.2011.A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery.Photogrammetric Engineering and Remote Sensing,77(7):721-732[DOI:10.14358/PERS.77.7.721]
Inglada J.2007.Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features.ISPRS Journal of Photogrammetry and Remote Sensing,62(3):236-248[DOI:10.1016/j.isprsjprs.2007.05.011]
Konstantinidis D,Stathaki T,Argyriou V and Grammalidis N.2017.Building detection using enhanced HOG-LBP features and region refinement processes.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,10(3):888-905[DOI:10.1109/JSTARS.2016.2602439]
Lee T S.1996.Image representation using 2D Gabor wavelets.IEEETransactions on Pattern Analysis and Machine Intelligence,18(10):959-971[DOI:10.1109/34.541406]
Li Y S,Tan Y H,Deng J J,Wen Q and Tian J W.2015.Cauchy graph embedding optimization for built-up areas detection from highresolution remote sensing images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(5):2078-2096[DOI:10.1109/JSTARS.2015.2394504]
Lin X G and Ning X G.2017.Extraction of human settlements from high resolution remote sensing imagery by fusing features of right angle corners and right angle sides.Acta Geodaetica et Cartographica Sinica,46(1):83-89(林祥国,宁晓刚.2017.融合直角点和直角边特征的高分辨率遥感影像居民点提取方法.测绘学报,46(1):83-89)[DOI:10.11947/j.AGCS.2017.20160389]
Liu G,Xia G S,Huang X,Yang W and Zhang L P.2013.A perceptioninspired building index for automatic built-up area detection in high-resolution satellite images//Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium.Melbourne,VIC:IEEE:3132-3135[DOI:10.1109/IGARSS.2013.6723490]
Ok A O.2013.Automated detection of buildings from single VHRmultispectral images using shadow information and graph cuts.IS-PRS Journal of Photogrammetry and Remote Sensing,86:21-40[DOI:10.1016/j.isprsjprs.2013.09.004]
Pesaresi M,Gerhardinger A and Kayitakire F.2008.A robust built-up area presence index by anisotropic rotation-invariant textural measure.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,1(3):180-192[DOI:10.1109/JSTARS.2008.2002869]
Shen X L,Shao Z F and Tian Y J.2014.Built-up areas extraction by textural feature and visual attention mechanism.Acta Geodaetica et Cartographica Sinica,43(8):842-847(沈小乐,邵振峰,田英洁.2014.纹理特征与视觉注意相结合的建筑区提取.测绘学报,43(8):842-847)[DOI:10.13485/j.cnki.11-2089.2014.0131]
Sirmacek B and Unsalan C.2009.Urban-area and building detection using SIFT keypoints and graph theory.IEEE Transactions on Geoscience and Remote Sensing,47(4):1156-1167[DOI:10.1109/TGRS.2008.2008440]
Sirmacek B and Unsalan C.2010.Urban area detection using local feature points and spatial voting.IEEE Geoscience and Remote Sensing Letters,7(1):146-150[DOI:10.1109/LGRS.2009.2028744]
Sun J Y,Huang Z J,Zhou S G,Xu N,Qian H M,Wang C L.2017.Building outline vectorization from high spatial resolution imagery.Journal of Remote Sensing,21(3):396-405(孙金彦,黄祚继,周绍光,徐南,钱海明,王春林.2017.高分辨率遥感影像中建筑物轮廓信息矢量化.遥感学报,21(3):396-405)[DOI:10.11834/jrs.20176127]
Tan Q L.2010.Urban building extraction from VHR multi-spectral images using object-based classification.Acta Geodaetica et Cartographica Sinica,39(6):618-623(谭衢霖.2010.高分辨率多光谱影像城区建筑物提取研究.测绘学报,39(6):618-623)
Tao C,Zou Z R and Ding X L.2014.Residential area detection from high-resolution remote sensing imagery using corner distribution.Acta Geodaetica et Cartographica Sinica,43(2):164-169,192(陶超,邹峥嵘,丁晓利.2014.利用角点进行高分辨率遥感影像居民地检测方法.测绘学报,43(2):164-169,192)[DOI:10.13485/j.cnki.11-2089.2014.0024]
Tournaire O,Brédif M,Boldo D and Durupt M.2010.An efficient stochastic approach for building footprint extraction from digital elevation models.ISPRS Journal of Photogrammetry and Remote Sensing,65(4):317-327[DOI:10.1016/j.isprsjprs.2010.02.002]
Turker M and Koc-San D.2015.Building extraction from high-resolution optical spaceborne images using the integration of support vector machine(SVM)classification,Hough transformation and perceptual grouping.International Journal of Applied Earth Observation and Geoinformation,34:58-69[DOI:10.1016/j.jag.2014.06.016]
Wang J,Yang X C,Qin X B,Ye X and Qin Q M.2015.An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery.IEEE Geoscience and Remote Sensing Letters,12(3):487-491[DOI:10.1109/LGRS.2014.2347332]
Wu W,Luo J C,Shen Z F and Zhu Z W.2012.Building extraction from high resolution remote sensing imagery based on spatialspectral method.Geomatics and Information Science of Wuhan University,37(7):800-805(吴炜,骆剑承,沈占锋,朱志文.2012.光谱和形状特征相结合的高分辨率遥感图像的建筑物提取方法.武汉大学学报(信息科学版),37(7):800-805)[DOI:10.13203/j.whugis2012.07.022]
Zhang H T and Li Y N.2015.Watershed algorithm with threshold mark for color image segmentation.Journal of Image and Graphics,20(12):1602-1611(张海涛,李雅男.2015.阈值标记的分水岭彩色图像分割.中国图象图形学报,20(12):1602-1611)[DOI:10.11834/jig.20151205]
Guo Q S,Wei Z W,Wang Y and Wang L.2017.The method of extracting spatial distribution characteristics of buildings combined with feature classification and proximity graph.Acta Geodaetica et Cartographica Sinica,46(5):631-638(郭庆胜,魏智威,王勇,王琳.2017.特征分类与邻近图相结合的建筑物群空间分布特征提取方法.测绘学报,46(5):631-638)[DOI:10.11947/j.AGCS.2017.20160374]
Hou Y,Zhou S L,Lei L and Zhao J.2013.Invariant feature with multicharacteristic scales using Gabor filter bank.Acta Electronica Sinica,41(6):1146-1152(侯毅,周石琳,雷琳,赵键.2013.基于Gabor滤波器组的多特征尺度不变特征提取方法.电子学报,41(6):1146-1152)[DOI:10.3969/j.issn.0372-2112.2013.06.016]
Hu X Y,Shen J J,Shan J and Pan L.2013.Local edge distributions for detection of salient structure textures and objects.IEEEGeoscience and Remote Sensing Letters,10(3):466-470[DOI:10.1109/LGRS.2012.2210188]
Huang X and Zhang L P.2011.A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery.Photogrammetric Engineering and Remote Sensing,77(7):721-732[DOI:10.14358/PERS.77.7.721]
Inglada J.2007.Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features.ISPRS Journal of Photogrammetry and Remote Sensing,62(3):236-248[DOI:10.1016/j.isprsjprs.2007.05.011]
Konstantinidis D,Stathaki T,Argyriou V and Grammalidis N.2017.Building detection using enhanced HOG-LBP features and region refinement processes.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,10(3):888-905[DOI:10.1109/JSTARS.2016.2602439]
Lee T S.1996.Image representation using 2D Gabor wavelets.IEEETransactions on Pattern Analysis and Machine Intelligence,18(10):959-971[DOI:10.1109/34.541406]
Li Y S,Tan Y H,Deng J J,Wen Q and Tian J W.2015.Cauchy graph embedding optimization for built-up areas detection from highresolution remote sensing images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(5):2078-2096[DOI:10.1109/JSTARS.2015.2394504]
Lin X G and Ning X G.2017.Extraction of human settlements from high resolution remote sensing imagery by fusing features of right angle corners and right angle sides.Acta Geodaetica et Cartographica Sinica,46(1):83-89(林祥国,宁晓刚.2017.融合直角点和直角边特征的高分辨率遥感影像居民点提取方法.测绘学报,46(1):83-89)[DOI:10.11947/j.AGCS.2017.20160389]
Liu G,Xia G S,Huang X,Yang W and Zhang L P.2013.A perceptioninspired building index for automatic built-up area detection in high-resolution satellite images//Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium.Melbourne,VIC:IEEE:3132-3135[DOI:10.1109/IGARSS.2013.6723490]
Ok A O.2013.Automated detection of buildings from single VHRmultispectral images using shadow information and graph cuts.IS-PRS Journal of Photogrammetry and Remote Sensing,86:21-40[DOI:10.1016/j.isprsjprs.2013.09.004]
Pesaresi M,Gerhardinger A and Kayitakire F.2008.A robust built-up area presence index by anisotropic rotation-invariant textural measure.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,1(3):180-192[DOI:10.1109/JSTARS.2008.2002869]
Shen X L,Shao Z F and Tian Y J.2014.Built-up areas extraction by textural feature and visual attention mechanism.Acta Geodaetica et Cartographica Sinica,43(8):842-847(沈小乐,邵振峰,田英洁.2014.纹理特征与视觉注意相结合的建筑区提取.测绘学报,43(8):842-847)[DOI:10.13485/j.cnki.11-2089.2014.0131]
Sirmacek B and Unsalan C.2009.Urban-area and building detection using SIFT keypoints and graph theory.IEEE Transactions on Geoscience and Remote Sensing,47(4):1156-1167[DOI:10.1109/TGRS.2008.2008440]
Sirmacek B and Unsalan C.2010.Urban area detection using local feature points and spatial voting.IEEE Geoscience and Remote Sensing Letters,7(1):146-150[DOI:10.1109/LGRS.2009.2028744]
Sun J Y,Huang Z J,Zhou S G,Xu N,Qian H M,Wang C L.2017.Building outline vectorization from high spatial resolution imagery.Journal of Remote Sensing,21(3):396-405(孙金彦,黄祚继,周绍光,徐南,钱海明,王春林.2017.高分辨率遥感影像中建筑物轮廓信息矢量化.遥感学报,21(3):396-405)[DOI:10.11834/jrs.20176127]
Tan Q L.2010.Urban building extraction from VHR multi-spectral images using object-based classification.Acta Geodaetica et Cartographica Sinica,39(6):618-623(谭衢霖.2010.高分辨率多光谱影像城区建筑物提取研究.测绘学报,39(6):618-623)
Tao C,Zou Z R and Ding X L.2014.Residential area detection from high-resolution remote sensing imagery using corner distribution.Acta Geodaetica et Cartographica Sinica,43(2):164-169,192(陶超,邹峥嵘,丁晓利.2014.利用角点进行高分辨率遥感影像居民地检测方法.测绘学报,43(2):164-169,192)[DOI:10.13485/j.cnki.11-2089.2014.0024]
Tournaire O,Brédif M,Boldo D and Durupt M.2010.An efficient stochastic approach for building footprint extraction from digital elevation models.ISPRS Journal of Photogrammetry and Remote Sensing,65(4):317-327[DOI:10.1016/j.isprsjprs.2010.02.002]
Turker M and Koc-San D.2015.Building extraction from high-resolution optical spaceborne images using the integration of support vector machine(SVM)classification,Hough transformation and perceptual grouping.International Journal of Applied Earth Observation and Geoinformation,34:58-69[DOI:10.1016/j.jag.2014.06.016]
Wang J,Yang X C,Qin X B,Ye X and Qin Q M.2015.An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery.IEEE Geoscience and Remote Sensing Letters,12(3):487-491[DOI:10.1109/LGRS.2014.2347332]
Wu W,Luo J C,Shen Z F and Zhu Z W.2012.Building extraction from high resolution remote sensing imagery based on spatialspectral method.Geomatics and Information Science of Wuhan University,37(7):800-805(吴炜,骆剑承,沈占锋,朱志文.2012.光谱和形状特征相结合的高分辨率遥感图像的建筑物提取方法.武汉大学学报(信息科学版),37(7):800-805)[DOI:10.13203/j.whugis2012.07.022]
Zhang H T and Li Y N.2015.Watershed algorithm with threshold mark for color image segmentation.Journal of Image and Graphics,20(12):1602-1611(张海涛,李雅男.2015.阈值标记的分水岭彩色图像分割.中国图象图形学报,20(12):1602-1611)[DOI:10.11834/jig.20151205]