收稿：2019-01-08，
纸质出版：2020-03-07
移动端阅览
及时准确地获取城市建筑区域的空间分布及其变化信息对于城市规划、空间地理数据库建设及区域社会经济分析具有重要意义。本文提出一种基于多尺度Gabor变换和感知聚类方法即张量投票 TV（Tensor Voting）相结合的自适应局部几何不变特征检测方法，并将其应用于高空间分辨率遥感影像建筑区域提取。首先，考虑到高分辨率遥感影像复杂的几何结构特征，使用Gabor滤波器组对影像进行多尺度多方向变换检测奇异性特征。然后，在感知聚类框架下，根据张量投票理论将不同方向子带系数位置编码为相应的二阶对称方向张量，为了突出影像几何特征，对不同尺度、不同方向子带中任意像素位置方向张量使用滤波器响应系数加权并求和完成多尺度特征融合。再次，对张量特征分解得到点结构与线结构显著性图并使用非极大抑制提取相应角点和曲线等局部几何特征，同时生成约束准则筛选角点以确定建筑物坐标。最后，利用概率密度估计结合局部角点特征生成全局概率密度场描述影像中像素从属于建筑目标的概率，并使用最大类间方差法（Otsu）阈值分割自动提取居民地多边形区域。使用分辨率分别为0.49 m、0.98 m的Google Earth及0.8 m的高分二号等影像数据集进行实验，实验结果表明本文方法相对于已有的Harris和HSCD点检测算法，在建筑区域提取质量上（Quality）上分别提高了4.79%，5.96%；1.47%，3.76%和1.91%，4.08%。
Based on characteristic of High Spatial Resolution (HSR) image
an adaptive local geometric invariant feature extraction method based on Gabor transform and Tensor Voting (TV) is proposed and applied to building area extraction from HSR remote sensing images. First
image geometric features are analyzed. Second
the feasibility of extracting built-up area using local feature points and probability density estimation are determined. This study provides specific methods and steps. First
considering the abundant geometric features of high spatial resolution remote sensing images
multi-scale and multi-direction Gabor filter banks are adopted in detecting the singularity of images contain building area. In order to extract edge information of buildings
only real part Gabor coefficients are used. Meanwhile
we also measured the influence of Gabor kernels of different size on geometric feature extraction experiments
and the optimal scale parameter intervals for geometric feature extraction of remote sensing images with different spatial resolutions are thus determined. Second
it is a fact that the response of Gabor filter at each pixel is a measure for orientation certainty
thus
we introduce the orientation tensor which represents an ideal direction in the direction of the unit vector perpendicular to frequency. By weighting the orientation tensor with Gabor coefficients ans summing over it to complete the information fusion
the resulting tensor givens an estimate for local orientation and orientation uncertainty at image position. The tensors are then used as an initial estimate for global context refinement using tensor voting and the points are classified based on the their likelihoods of being part of a feature type
non-maximal suppression is used to extract geometric features. A key advantage of combining the Gabor filtering and tensor voting is that it eliminates the need for any thresholds therefore removing any data dependencies. In order to achieve a reliable extraction of local invariant feature such as corners from built-up area
three criterions are further proposed to refine the first stage result. Finally
each local building corner indicates a building be detected in image. However
only one of them is not sufficient alone to detecting a building. In fact
the more local points a buildings has
the more probable its detection becomes. Based on that fact
a probability density estimate method is generated to describe the probability that the pixel belongs to the building area
and the Otsu method is used to automatically extract the polygon area of the residential area. Experiments were carried out using image data sets such as Google and GF-2 with resolution higher than 1 meter
results showing that the proposed method can achieve higher accuracy in building area detection compared with the state-of-the-art corner detection algorithm such as Harris corner and High-speed corner detect method.
Cheng Y X , Qin K , Zhang Y and Yuan Y . 2017 . A residential area extraction method for high resolution remote sensing imagery by using visual saliency and perceptual organization . Acta Geodaetica et Cartographica Sinica , 46 ( 12 ): 1959 - 1968
陈一祥 , 秦昆 , 张晔 , 袁媛 . 2017 . 视觉显著性与知觉组织相结合的高分影像居民地提取方法 . 测绘学报 , 46 ( 12 ): 1959-1968 [ DOI: 10.11947/j.AGCS.2017.20170176 http://dx.doi.org/10.11947/j.AGCS.2017.20170176 ]
Bhatti S S and Tripathi N K . 2014 . Built-up area extraction using landsat 8 OLI imagery . GIScience and Remote Sensing , 51 ( 4 ): 445 - 467 [ DOI: 10.1080/15481603.2014.939539 http://dx.doi.org/10.1080/15481603.2014.939539 ]
Zhang J , Li P J and Xu H Q . 2013 . Urban built-up area extraction using combined spectral information and multivariate texture // Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium . Melbourne, VIC, Australia : IEEE : 4249 - 4252 [ DOI: 10.1109/IGARSS.2013.6723772 http://dx.doi.org/10.1109/IGARSS.2013.6723772 ]
You Y F , Wang S Y , Wang B , Ma Y X , Sheng M , Liu W H , Xiao L . 2019 . Study on hierarchical building extraction from high resolution remote sensing imagery , 23 ( 1 ): 125 - 136
游永发 , 王思远 , 王斌 , 马元旭 , 申明 , 刘卫华 , 肖琳 . 2019 . 高分辨率遥感影像建筑物分级提取 . 遥感学报 , 23 ( 1 ): 125 - 136
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 high-resolution 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 http://dx.doi.org/10.1109/JSTARS.2015.2394504 ]
Tao C , Tan Y H , Zou Z R and Tian J W . 2013 . Unsupervised detection of built-up areas from multiple high-resolution remote sensing images . IEEE Geoscience and Remote Sensing Letters , 10 ( 6 ): 1300 - 1304 [ DOI: 10.1109/LGRS.2013.2237751 http://dx.doi.org/10.1109/LGRS.2013.2237751 ]
Li P C , Xing S , Xu Q , Zhou Y , Liu Z Q , Zhang Y , Geng X . 2014 . An automated approach for complex shape building reconstruction with key point detection . Journal of Remote Sensing , 18 ( 6 ): 1237 - 1246
李鹏程 , 邢帅 , 徐青 , 周杨 , 刘志青 , 张艳 , 耿迅 . 2014 . 关键点检测的复杂建筑物模型自动重建 . 遥感学报 , 18 ( 6 ): 1237 - 1246
Liu Y B , Zhang Z X , Zhong R F , Chen D , Ke Y H , Peethambaran J , Chen C Q and Sun L . 2018 . Multilevel building detection framework in remote sensing images based on convolutional neural networks . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 10 ): 3688 - 3700 [ DOI: 10.1109/JSTARS.2018. 2866284 http://dx.doi.org/10.1109/JSTARS.2018. 2866284 ]
Zhao L J , Qin Y L , Gao G and Kuang G Y . 2009 . Detection of built-up areas from high-resolution SAR images using the GLCM textural analysis. Journal of Remote Sensing, 13(3): 483-490 (赵凌君, 秦玉亮, 高贵, 匡纲要. 2009. 利用GLCM纹理分析的高分辨率SAR图像建筑区检测 [J]. 遥感学报 , 13 ( 3 ): 483 - 490
[DOI: 10.3321/j.issn:1007-4619. 2009.03.011]
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 http://dx.doi.org/10.1109/JSTARS.2008.2002869 ]
Krishnamachari S and Chellappa R . 1996 . Delineating buildings by grouping lines with mrfs . IEEE Transactions on Image Processing , 5 ( 1 ): 164 - 168 [ DOI: 10.1109/83.481683 http://dx.doi.org/10.1109/83.481683 ]
Su W , Li J , Chen Y H , Liu Z G , Zhang J S , Low T M , Suppiah I and Hashim S A M . 2008 . Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery . International Journal of Remote Sensing , 29 ( 11 ): 3105 - 3117 [ DOI: 10.1080/01431160701469016 http://dx.doi.org/10.1080/01431160701469016 ]
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 http://dx.doi.org/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 http://dx.doi.org/10.1109/LGRS.2009.2028744 ]
Sirmacek B and Unsalan C . 2011 . A probabilistic framework to detect buildings in aerial and satellite images . IEEE Transactions on Geoscience and Remote Sensing , 49 ( 1 ): 211 - 221 [ DOI: 10.1109/TGRS.2010.2053713 http://dx.doi.org/10.1109/TGRS.2010.2053713 ]
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 http://dx.doi.org/10.13485/j.cnki.11-2089. 2014.0024 ]
M. Idrissa , V. Lacroix , A. Hincq , H. Bruynseels , and O. Swartenbroekx ,“ SPOT 5 images for urbanization detection,
” in Proc . Adv . Concepts Intell.Vis. Syst. , 2004 .
Medioni G , Lee M S and Tang C K . 2000 . A Computational Framework for Segmentation and Grouping . The Netherlands : Elsevier Science
Kim H S , Choi H K and Lee K H . 2009 . Feature detection of triangular meshes based on tensor voting theory . Computer-Aided Design , 41 ( 1 ): 47 - 58 [ DOI: 10.1016/j.cad.2008.12.003 http://dx.doi.org/10.1016/j.cad.2008.12.003 ]
Moreno R , Garcia M A and Puig D . 2010 . Robust color image segmentation through tensor voting // Proceedings of the 20th International Conference on Pattern Recognition . Istanbul, Turkey : IEEE : 3372 - 3375 [ DOI: 10.1109/ICPR. 2010.823 http://dx.doi.org/10.1109/ICPR. 2010.823 ]
Poullis C and You S Y . 2010 . Delineation and geometric modeling of road networks . ISPRS Journal of Photogrammetry and Remote Sensing , 65 ( 2 ): 165 - 181 [ DOI: 10.1016/j.isprsjprs.2009.10.004 http://dx.doi.org/10.1016/j.isprsjprs.2009.10.004 ]
Harris C and Stephens M . 1988 . A combined corner and edge detector // Proceedings of the Alvey Vision Conference . Manchester : Alvety Vision Club [ DOI: 10.5244/C.2.23 http://dx.doi.org/10.5244/C.2.23 ]
Wiedemann C , Heipke C , Mayer H and Jamet O . 1998 . Empirical evaluation of automatically extracted road axes // Proceedings of the 9th Australasian Remote Sensing Photogrammetry Conference . Sydney : The University of New South Wales : 172 - 187
Rosten E and Drummond T . 2006 . Machine learning for high-speed corner detection // Proceedings of the 9th European Conference on Computer Vision . Graz, Austria : Springer : 430 - 443 [ DOI: 10.1007/11744023_34 http://dx.doi.org/10.1007/11744023_34 ]
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