收稿：2020-02-18，
纸质出版：2021-11-07
移动端阅览
房屋建筑物作为人类活动的主要场所，快速准确地将其从高分辨率遥感影像中提取出来，对促进遥感信息在防灾减灾、城镇管理等方面的应用具有重要意义。本文基于深度学习，提出了高分辨率遥感影像房屋建筑物像素级精确提取方法。首先，针对样本图像边缘像素特征不足现象，以U-Net模型为基础提出IEU-Net模型，设计了全新的忽略边缘交叉熵函数IELoss并将其作为损失函数，另外添加Dropout和BN层在避免过拟合的同时提高模型训练速度和鲁棒性。其次，为解决模型特征丰富度有限的问题，引入形态学建筑物指数MBI，与遥感影像RGB波段一同参与到模型的分类过程。最后，在模型预测时与IELoss相对应采用忽略边缘预测策略从而获得最佳建筑物提取结果。实验对比分析表明：本文方法能有效克服样本边缘像素特征不足问题并抑制道路、建筑物阴影对结果的影响，提升高分辨率遥感影像中房屋建筑物的提取精度。
House buildings
as the main place for human activities
can be rapidly and accurately extracted from high-resolution remote sensing images
which is of great significance for promoting remote sensing information in disaster prevention and mitigation and town management. On the basis of deep learning
this paper proposes a pixel-level accurate extraction method for house buildings by using high-resolution remote sensing images.
First
in consideration of the lack of pixel features at the edge of a sample image
an IEU-Net model is proposed on the basis of the U-Net model. A new ignore-edges categorical cross entropy function
IELoss
is designed as a loss function. Dropout and BN layers are added to help improve the speed and robustness of model training while fitting. Second
to solve the problem of limited feature richness of the model
the Morphological Building Index (MBI) is introduced into the classification process of the model
together with the remote sensing image RGB band. Lastly
when ignoring the edge prediction method
the best building extraction results can be obtained in the model prediction that corresponds to the IEU-Net model. The remote sensing image data used in this study are 0.8 m true color and infrared band data obtained by multispectral and panchromatic fusion from the GF-2 satellite of the Yushu Tibetan Autonomous Prefecture of Qinghai Province. A label image is obtained by visually interpreting the images by using ArcGIS software and performing a feature-to-raster transformation on the obtained vector file. Then
a ground truth image of the location of houses and buildings in the remote sensing image is recorded. The MBI and RGB bands are fused
and the corresponding label images are randomly cropped to obtain 500 pieces of 256×256 training data and 100 pieces of 256×256 verification data. Regarding the IEU-Net model
r
value of 0.5 and the training data as the model input and using the Adam optimization algorithm for back propagation iteration for 100 times help achieve the optimal parameter results.
The Overall Accuracy (OA) is 91.86%
and the kappa value is 0.802. To verify the effectiveness of IELoss in the IEU-Net model and the ignore-edges prediction method relative to CELoss and the ordinary prediction method in solving the problem of insufficient edge pixel features
this study continues the comparison by using the values of
r
of 1.0
0.9
0.8
0.7
and 0.6 (when
r
is 1.0
IELoss is equivalent to CELoss
and the ignore-edges prediction method is equivalent to the ordinary prediction method). The results show that the OA of the model using IELoss and the ignore-edges prediction method is 5.03% higher than that of the model using CELoss and the ordinary prediction method
and the kappa value is 0.165 higher. To verify the effectiveness of adding MBI to the training data of the model
a comparative experiment is performed by using RGB three-band data as the training set. The prediction result obtained using the MBI-added data set is 1.55% higher than the OA of the RGB band data set. The kappa value is increased by 0.009.
The experimental results show that the IEU-Net model effectively solves the problem of insufficient edge pixel features and achieves house and building extraction accuracy with high OA and kappa values. The addition of MBI data can overcome the influence of roads and house building shadows to a certain extent and accurately extract the edge information of house buildings. Therefore
if significant vegetation exists near the houses and buildings in our area of interest
we can consider adding the normalized difference vegetation index to the training set to reduce the impact of the vegetation.
Audebert N , Le Saux B and Lefèvre S . 2017 . Semantic segmentation of earth observation data using multimodal and multi-scale deep networks // 13th Asian Conference on Computer Vision . Taipei, China : Springer : 180 - 196 [ DOI: 10.1007/978-3-319-54181-5_12 http://dx.doi.org/10.1007/978-3-319-54181-5_12 ]
Chen K Q , Fu K , Yan M L , Gao X , Sun X and Wei X . 2018 . Semantic segmentation of aerial images with shuffling convolutional neural networks . IEEE Geoscience and Remote Sensing Letters , 15 ( 2 ): 173 - 177 [ DOI: 10.1109/LGRS.2017.2778181 http://dx.doi.org/10.1109/LGRS.2017.2778181 ]
Chen L C , Zhu Y K , Papandreou G , Schroff F and Adam H . 2018 . Encoder-decoder with atrous separable convolution for semantic image segmentation // 15th European Conference on Computer Vision . Munich : Springer : 833 - 851 [ DOI: 10.1007/978-3-030-01234-2_49 http://dx.doi.org/10.1007/978-3-030-01234-2_49 ]
Fan R S , Chen Y , Xu Q H and Wang J X . 2019 . A high-resolution remote sensing image building extraction method based on deep learning . Acta Geodaetica et Cartographica Sinica , 48 ( 1 ): 34 - 41
范荣双 , 陈洋 , 徐启恒 , 王竞雪 . 2019 . 基于深度学习的高分辨率遥感影像建筑物提取方法 . 测绘学报 , 48 ( 1 ): 34- 41 [ DOI: 10.11947/j.AGCS.2019.20170638 http://dx.doi.org/10.11947/j.AGCS.2019.20170638 ]
Galvanin E and Dal Poz A P P . 2012 . Extraction of building roof contours from LiDAR data using a Markov-random-field-based approach . IEEE Transactions on Geoscience and Remote Sensing , 50 ( 3 ): 981 - 987 [ DOI: 10.1109/tgrs.2011.2163823 http://dx.doi.org/10.1109/tgrs.2011.2163823 ]
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 http://dx.doi.org/10.14358/PERS.77.7.721 ]
Ioffe S and Szegedy C . 2015 . Batch normalization: accelerating deep network training by reducing internal covariate shift // Proceedings of the 32nd International Conference on International Conference on Machine Learning . Lille : JMLR.org : 448 - 456
Jin X Y and Davis C H . 2005 . Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information . EURASIP Journal on Advances in Signal Processing , 2005 ( 14 ): 2196 - 2206 [ DOI: 10.1155/asp.2005.2196 http://dx.doi.org/10.1155/asp.2005.2196 ]
Kabolizade M , Ebadi H and Ahmadi S . 2010 . An improved snake model for automatic extraction of buildings from urban aerial images and LiDAR data . Computers, Environment and Urban Systems , 34 ( 5 ): 435 - 441 [ DOI: 10.1016/j.compenvurbsys.2010.04.006 http://dx.doi.org/10.1016/j.compenvurbsys.2010.04.006 ]
Kang W C , Xiang Y M , Wang F and You H J . 2019 . EU-Net: an efficient fully convolutional network for building extraction from optical remote sensing images . Remote Sensing , 11 ( 23 ): 2813 [ DOI: 10.3390/rs11232813 http://dx.doi.org/10.3390/rs11232813 ]
Mnih V . 2013 . Machine Learning for Aerial Image Labeling . Toronto : University of Toronto
Ronneberger O , Fischer P and Brox T . 2015 . U-Net: convolutional networks for biomedical image segmentation // 18th International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich : Springer : 234 - 241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]
Saito S , Yamashita T and Aoki Y . 2016 . Multiple object extraction from aerial imagery with convolutional neural networks //Electronic Imaging, Intelligent Robots and Computer Vision XXXIII: Algorithms and Techniques. [s.l.]: Society for Imaging Science and Technology : 1 - 9 [ DOI: 10.2352/issn.2470-1173.2016.10.robvis-392 http://dx.doi.org/10.2352/issn.2470-1173.2016.10.robvis-392 ]
Shelhamer E , Long J and Darrell T . 2017 . Fully convolutional networks for semantic segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence , 39 ( 4 ): 640 - 651 [ DOI: 10.1109/TPAMI.2016.2572683 http://dx.doi.org/10.1109/TPAMI.2016.2572683 ]
Song J E , Wang X and Li P J . 2012 Urban building damage detection from VHR imagery by including temporal and spatial texture features . Journal of Remote Sensing , 16 ( 6 ): 1233 - 1245
宋桔尔 , 王雪 , 李培军 . 2012 . 加入空间-时相纹理的高分辨率图像城市建筑物物倒塌提取 . 遥感学报 , 16 ( 6 ): 1233- 1245 [ DOI: 10.11834/jrs.20121277 http://dx.doi.org/10.11834/jrs.20121277 ]
Srivastava N , Hinton G , Krizhevsky A , Sutskever I and Salakhutdinov R . 2014 . Dropout: a simple way to prevent neural networks from overfitting . The Journal of Machine Learning Research , 15 ( 1 ): 1929 - 1958
Sun X , Fu K , Long H , Hu Y F , Cai L and Wang H Q . 2009 . Contextual models for automatic building extraction in high resolution remote sensing image using object-based boosting method //IEEE International Geoscience and Remote Sensing Symposium. Boston: IEEE: II-437-II-440 [ DOI: 10.1109/IGARSS.2008.4779022 http://dx.doi.org/10.1109/IGARSS.2008.4779022 ]
Tao C , Tan Y H , Cai H J , Du B and Tian J W . 2010 . Object-oriented method of hierarchical urban building extraction from high-resolution remote-sensing imagery . Acta Geodaetica et Cartographica Sinica , 39 ( 1 ): 39 - 45
陶超 , 谭毅华 , 蔡华杰 , 杜博 , 田金文 . 2010 . 面向对象的高分辨率遥感影像城区建筑物分级提取方法 . 测绘学报 , 39 ( 1 ): 39 - 45
Tian J J and Reinartz P . 2013 . Fusion of multi-spectral bands and DSM from Worldview-2 Stereo imagery for building extraction // Joint Urban Remote Sensing Event . Sao Paulo : IEEE : 135 - 138 [ DOI: 10.1109/JURSE.2013.6550684 http://dx.doi.org/10.1109/JURSE.2013.6550684 ]
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 http://dx.doi.org/10.1016/j.jag.2014.06.016 ]
You Y F , Wang S Y , Wang B , Ma Y X , Shen M , Liu W H and Xiao L . 2019 . Study on hierarchical building extraction from high resolution remote sensing imagery . Journal of Remote Sensing , 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 [ DOI: 10.11834/jrs.20197500 http://dx.doi.org/10.11834/jrs.20197500 ]
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