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Received：16 May 2018，
Published：07 January 2020
移动端阅览
遥感影像建筑物提取具有重要的应用价值。然而，高分辨率遥感影像中细节信息繁多、特征复杂，增加了建筑物提取难度。针对这一问题，本文提出一种基于多尺度SLIC-GMRF和FCNSVM的建筑物提取方法，一定程度上提高了高分辨率遥感影像建筑物提取能力。首先，利用多尺度SLIC-GMRF分割算法确定初始建筑物区域，然后，充分利用FCN神经网络在语义分割中的优势抽取建筑物特征，最后，结合提取出的建筑物特征训练SVM分类器细化建筑物提取结果，通过3种控制实验，两种对比方法得出以下结论：SLIC分割算法影响初始分割结果；SVM分类器影响建筑物细部提取；FCN特征影响SVM分类器性能。对于特征清晰、遮挡干扰较少的研究区，本文方法能够较好提取影像中的建筑物，查准率、查全率、质量指标均优于对比方法，对建筑物复杂分布的研究区同样能够取得较好的提取效果。
The extraction of buildings from remote sensing imagery has an important application value. However
high-resolution images contain detailed information and complex features that hinder the difficulty of building extraction process.
To address this problem
we propose a building extraction method of building extraction based on multi-scale SLIC-GMRF and FCNSVM that demonstrates an improved ability of extracting buildings from high-resolution remote sensing images to some extent. First
a multi-scale SLIC-GMRF segmentation algorithm is applied to determine the initial building area
and then the advantages of the FCN neural network in semantic segmentation are utilized to extract the building features. Second
the extracted building features are used to train an SVM classifier to refine the building extraction results of building.
The results of three control experiments and two comparative tests reveal that the SLIC segmentation algorithm affects the initial segmentation results
the SVM classifier affects the extraction of building details
and the FCN features influence the performance of the SVM classifier. The precision rate
recall rate
and quality index of the proposed method are all better than the compared methods.
The following conclusions can be drawn from the experimental results. For the study area with clear features and minimal obstructions
the proposed method can effectively extract buildings from an image. This method can also obtain ideal results for areas with a complex distribution of buildings can also get ideal results.
Achanta R , Shaji A , Smith K , Lucchi A , Fua P and Süsstrunk S . 2010 . SLIC Superpixels . EPFL Technical Report no . 149300 . EPFL
Achanta R , Shaji A , Smith K , Lucchi A , Fua P and Süsstrunk S . 2012 . SLIC superpixels compared to state-of-the-art superpixel methods . IEEE Transactions on Pattern Analysis and Machine Intelligence , 34 ( 11 ): 2274 - 2282 [ DOI: 10.1109/TPAMI.2012.120 http://dx.doi.org/10.1109/TPAMI.2012.120 ]
Benedek C , Shadaydeh M , Kato Z , Szirányi T and Zerubia J . 2015 . Multilayer Markov random field models for change detection in optical remote sensing images . ISPRS Journal of Photogrammetry and Remote Sensing , 107 : 22 - 37 [ DOI: 10.1016/j.isprsjprs.2015.02.006 http://dx.doi.org/10.1016/j.isprsjprs.2015.02.006 ]
Bengio Y . 2012 . Practical recommendations for gradient-based training of deep architectures //Montavon G, Orr G B and Müller K B, eds. Neural Networks: Tricks of the Trade . Berlin, Heidelberg : Springer : 437 - 478 [ DOI: 10.1007/978-3-642-35289-8_26 http://dx.doi.org/10.1007/978-3-642-35289-8_26 ]
Bengio Y , Courville A and Vincent P . 2013 . Representation learning: a review and new perspectives . IEEE Transactions on Pattern Analysis and Machine Intelligence , 35 ( 8 ): 1798 - 1828 [ DOI: 10.1109/TPAMI.2013.50 http://dx.doi.org/10.1109/TPAMI.2013.50 ]
Besag J . 1986 . On the statistical analysis of dirty pictures . Journal of the Royal Statistical Society. Series B : Methodological , 48 ( 3 ): 259 - 302
Bouman C and Liu B . 1991 . Multiple resolution segmentation of textured images . IEEE Transactions on Pattern Analysis and Machine Intelligence , 13 ( 2 ): 99 - 113 [ DOI: 10.1109/34.67641 http://dx.doi.org/10.1109/34.67641 ]
Bouman C A and Shapiro M . 1994 . A multiscale random field model for bayesian image segmentation . IEEE Transactions on Image Processing , 3 ( 2 ): 162 - 177 [ DOI: 10.1109/83.277898 http://dx.doi.org/10.1109/83.277898 ]
Bruzzone L and Prieto D F . 2000 . Automatic analysis of the difference image for unsupervised change detection . IEEE Transactions on Geoscience and Remote Sensing , 38 ( 3 ): 1171 - 1182 [ DOI: 10.1109/36.843009 http://dx.doi.org/10.1109/36.843009 ]
Canziani A , Paszke A and Culurciello E . 2016 . An analysis of deep neural network models for practical applications // Proceedings of 2016 ICLR . San Juan : ICLR
Chang C C and Lin C J . 2011 . LIBSVM: a library for support vector machines . ACM Transactions on Intelligent Systems and Technology , 2 ( 3 ): 27 [ DOI: 10.1145/1961189.1961199 http://dx.doi.org/10.1145/1961189.1961199 ]
Collobert R and Weston J . 2008 . A unified architecture for natural language processing: deep neural networks with multitask learning // Proceedings of the 25th International Conference on Machine Learning . Helsinki, Finland : ACM : 160 - 167 [ DOI: 10.1145/1390156.1390177 http://dx.doi.org/10.1145/1390156.1390177 ]
Cortes C and Vapnik V . 1995 . Support-vector networks . Machine Learning , 20 ( 3 ): 273 - 297 [ DOI: 10.1007/BF00994018 http://dx.doi.org/10.1007/BF00994018 ]
Drăguţ L , Tiede D and Levick S R . 2010 . ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data . International Journal of Geographical Information Science , 24 ( 6 ): 859 - 871 [ DOI: 10.1080/13658810903174803 http://dx.doi.org/10.1080/13658810903174803 ]
Drăguţ L , Csillik O , Eisank C and Tiede D . 2014 . Automated parameterisation for multi-scale image segmentation on multiple layers . ISPRS Journal of Photogrammetry and Remote Sensing , 88 : 119 - 127 [ DOI: 10.1016/j.isprsjprs.2013.11.018 http://dx.doi.org/10.1016/j.isprsjprs.2013.11.018 ]
Felzenszwalb P F and Huttenlocher D P . 2004 . Efficient graph-based image segmentation . International Journal of Computer Vision , 59 ( 2 ): 167 - 181 [ DOI: 10.1023/B:VISI.0000022288.19776.77 http://dx.doi.org/10.1023/B:VISI.0000022288.19776.77 ]
Gao B C . 1996 . NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space . Remote Sensing of Environment , 58 ( 3 ): 257 - 266 [ DOI: 10.1016/S0034-4257(96)00067-3 http://dx.doi.org/10.1016/S0034-4257(96)00067-3 ]
Geman S and Geman D . 1984 . Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images . IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI- 6 ( 6 ): 721 - 741 [ DOI: 10.1109/TPAMI.1984.4767596 http://dx.doi.org/10.1109/TPAMI.1984.4767596 ]
Gong M G , Yang H L and Zhang P Z . 2017 . Feature learning and change feature classification based on deep learning for ternary change detection in SAR images . ISPRS Journal of Photogrammetry and Remote Sensing , 129 : 212 - 225 [ DOI: 10.1016/j.isprsjprs.2017.05.001 http://dx.doi.org/10.1016/j.isprsjprs.2017.05.001 ]
Grinias I , Panagiotakis C and Tziritas G . 2016 . MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images . ISPRS Journal of Photogrammetry and Remote Sensing , 122 : 145 - 166 [ DOI: 10.1016/j.isprsjprs.2016.10.010 http://dx.doi.org/10.1016/j.isprsjprs.2016.10.010 ]
He K M , Zhang X Y , Ren S Q and Sun J . 2016 . Deep residual learning for image recognition // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV, United States : IEEE : 770 - 778 [ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Heipke C , Mayer H , Wiedemann C and Jamet O . 1997 . Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing, 32(3- 2 W 3 ): 47 - 56
Hinton G , Li D , Dong Y , Dahl G E , Mohamed A R , Jaitly N , Senior A , Vanhoucke V , Nguyen P , Sainath T N and Kingsbury B . 2012 . Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups . IEEE Signal Processing Magazine , 29 ( 6 ): 82 - 97 [ DOI: 10.1109/MSP.2012.2205597 http://dx.doi.org/10.1109/MSP.2012.2205597 ]
Huang X and Zhang L P . 2012 . Morphological building/shadow index for building extraction from high-resolution imagery over urban areas . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 5 ( 1 ): 161 - 172 [ DOI: 10.1109/JSTARS.2011.2168195 http://dx.doi.org/10.1109/JSTARS.2011.2168195 ]
Jia Y Q , Shelhamer E , Donahue J , Karayev S , Long J , Girshick R , Guadarrama S and Darrell T . 2014 . Caffe: convolutional architecture for fast feature embedding // Proceedings of the 22nd ACM International Conference on Multimedia . Orlando : ACM : 675 - 678 [ DOI: 10.1145/2647868.2654889 http://dx.doi.org/10.1145/2647868.2654889 ]
Kaufman L and Rousseeuw P J . 1990 . Finding Groups in Data: An Introduction to Cluster Analysis . New York : John Wiley and Sons : 111 - 112 [ DOI: 10.1002/9780470316801 http://dx.doi.org/10.1002/9780470316801 ]
Krishnamachari S and Chellappa R . 1997 . Multiresolution gauss-Markov random field models for texture segmentation . IEEE Transactions on Image Processing , 6 ( 2 ): 251 - 267 [ DOI: 10.1109/83.551696 http://dx.doi.org/10.1109/83.551696 ]
Krizhevsky A , Sutskever I and Hinton G E . 2012 . ImageNet classification with deep convolutional neural networks // Proceedings of the 25th International Conference on Neural Information Processing Systems . Lake Tahoe : ACM : 1097 - 1105
Laptev D , Savinov N , Buhmann J M and Pollefeys M . 2016 . TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, NV : IEEE : 289 - 297 [ DOI: 10.1109/CVPR.2016.38 http://dx.doi.org/10.1109/CVPR.2016.38 ]
Levinshtein A , Stere A , Kutulakos K N , Fleet D J , Dickinson S J and Siddiqi K . 2009 . TurboPixels: fast superpixels using geometric flows . IEEE Transactions on Pattern Analysis and Machine Intelligence , 31 ( 12 ): 2290 - 2297 [ DOI: 10.1109/TPAMI.2009.96 http://dx.doi.org/10.1109/TPAMI.2009.96 ]
Li S Z . 1994 . Markov random field models in computer vision // Proceedings of the 3rd European Conference on Computer Vision . Stockholm, Sweden : Springer-Verlag : 361 - 370 [ DOI: 10.1007/BFb0028368 http://dx.doi.org/10.1007/BFb0028368 ]
Li X W and Wang Y T . 2013 . Prospects on future developments of quantitative remote sensing . Acta Geographica Sinica , 68 ( 9 ): 1163 - 1169
李小文 , 王祎婷 . 2013 . 定量遥感尺度效应刍议 . 地理学报 , 68 ( 9 ): 1163-1169 [ DOI: 10.11821/dlxb201309001 http://dx.doi.org/10.11821/dlxb201309001 ]
Liasis G and Stavrou S . 2016 . Building extraction in satellite images using active contours and colour features . International Journal of Remote Sensing , 37 ( 5 ): 1127 - 1153 [ DOI: 10.1080/01431161.2016.1148283 http://dx.doi.org/10.1080/01431161.2016.1148283 ]
Liu S Y and Deng W H . 2016 . Very deep convolutional neural network based image classification using small training sample size // Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition . Kuala Lumpur, Malaysia : IEEE : 730 - 734 [ DOI: 10.1109/ACPR.2015.7486599 http://dx.doi.org/10.1109/ACPR.2015.7486599 ]
Long J , Shelhamer E and Darrell T . 2015 . Fully convolutional networks for semantic segmentation // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston, Massachusetts : IEEE : 3431 - 3440 [ DOI: 10.1109/CVPR.2015.7298965 http://dx.doi.org/10.1109/CVPR.2015.7298965 ]
Mnih V . 2013 . Machine Learning for Aerial Image Labeling . Toronto : University of Toronto
Moore A P , Prince S J D , Warrell J , Mohammed U and Jones G . 2008 . Superpixel lattices // Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition . Anchorage, AK : IEEE : 1 - 8 [ DOI: 10.1109/CVPR.2008.4587471 http://dx.doi.org/10.1109/CVPR.2008.4587471 ]
Ren X F and Malik J . 2003 . Learning a classification model for segmentation // Proceedings of the 9th IEEE International Conference on Computer Vision . Nice, France : IEEE : 10 - 17 [ DOI: 10.1109/ICCV.2003.1238308 http://dx.doi.org/10.1109/ICCV.2003.1238308 ]
Rignot E and Chellappa R . 1992 . Segmentation of polarimetric synthetic aperture radar data . IEEE Transactions on Image Processing , 1 ( 3 ): 281 - 300 [ DOI: 10.1109/83.148603 http://dx.doi.org/10.1109/83.148603 ]
Romero A , Gatta C and Camps-Valls G . 2016 . Unsupervised deep feature extraction for remote sensing image classification . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 3 ): 1349 - 1362 [ DOI: 10.1109/TGRS.2015.2478379 http://dx.doi.org/10.1109/TGRS.2015.2478379 ]
Rouse J W , Hass R H , Schell J A , Deering D W and Harlan J C . 1974 . Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation . NASA/GSFC Type III Final Report. Greenbelt, MD
Russakovsky O , Deng J , Su H , Krause J , Satheesh S , Ma S , Huang Z H , Karpathy A , Khosla A , Bernstein M , Berg A C and Li F F . 2015 . ImageNet large scale visual recognition challenge . International Journal of Computer Vision , 115 ( 3 ): 211 - 252 [ DOI: 10.1007/s11263-015-0816-y http://dx.doi.org/10.1007/s11263-015-0816-y ]
Saito S , Yamashita T and Aoki Y . 2016 . Multiple object extraction from aerial imagery with convolutional neural networks . Journal of Imaging Science and Technology , 60 ( 1 ): 10402-1-10402 - 9 [ DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010402 http://dx.doi.org/10.2352/J.ImagingSci.Technol.2016.60.1.010402 ]
Sermanet P , Eigen D , Zhang X , Mathieu M , Fergus R and LeCun Y . 2013 . OverFeat: integrated recognition, localization and detection using convolutional networks . arXiv : 1312 . 6229
Sharma A , Liu X W , Yang X J and Shi D . 2017 . A patch-based convolutional neural network for remote sensing image classification . Neural Networks , 95 : 19 - 28 [ DOI: 10.1016/j.neunet.2017.07.017 http://dx.doi.org/10.1016/j.neunet.2017.07.017 ]
Shi J B and Malik J . 2000 . Normalized cuts and image segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence , 22 ( 8 ): 888 - 905 [ DOI: 10.1109/34.868688 http://dx.doi.org/10.1109/34.868688 ]
Simonyan K and Zisserman A . 2015 . Very deep convolutional networks for large-scale image recognition // Proceedings of 2015 ICLR . San Diego, California : [s.n.]: 1409-1556
Socher R , Lin C C Y , Ng A Y and Manning C D . 2011 . Parsing natural scenes and natural language with recursive neural networks // Proceedings of the 28th International Conference on Machine Learning . Bellevue, USA : Omnipress : 129 - 136
Volpi M and Tuia D . 2017 . Dense semantic labeling of subdecimeter resolution images with convolutional neural networks . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 2 ): 881 - 893 [ DOI: 10.1109/TGRS.2016.2616585 http://dx.doi.org/10.1109/TGRS.2016.2616585 ]
Wickham H . 2011 . The split-apply-combine strategy for data analysis . Journal of Statistical Software , 40 ( 1 ): 1 - 29
Yang Y and Newsam S . 2010 . Bag-of-visual-words and spatial extensions for land-use classification // Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems . San Jose, California : ACM : 270 - 279 [ DOI: 10.1145/1869790.1869829 http://dx.doi.org/10.1145/1869790.1869829 ]
Yu Q Y and Clausi D A . 2006 . Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 12 ): 3687 - 3694 [ DOI: 10.1109/TGRS.2006.885046 http://dx.doi.org/10.1109/TGRS.2006.885046 ]
Zhang H Y , Wang L W and Chen Y X . 2013 . Research progress of probabilistic graphical models: a survey . Journal of Software , 24 ( 11 ): 2476 - 2497
张宏毅 , 王立威 , 陈瑜希 . 2013 . 概率图模型研究进展综述 . 软件学报 , 24 ( 11 ): 2476-2497 [ DOI: 10.3724/SP.J.1001.2013.04486 http://dx.doi.org/10.3724/SP.J.1001.2013.04486 ]
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