一种基于边界感知的遥感影像建筑物提取方法

doi:10.19665/j.issn1001-2400.2022.01.025

Abstract

Abstract:

The complexity of remote sensing images brings a great challenge for building extraction research.The introduction of deep learning improves the accuracy of building extraction from remote sensing images,but there are still some problems such as blurred boundaries,missing targets and incomplete extraction areas.To address these issues,this paper proposes a boundary-aware network for building extraction from remote sensing images,including the feature fusion network,feature enhancement network and feature refinement network.First,the feature fusion network uses the encoding-decoding structure to extract different scale features,and designs the interactive aggregation module to fuse different scale features.Then,the feature enhancement network enhances the learning of missed targets through subtraction and cascade operation to obtain more comprehensive features.Finally,the feature refinement network further refines the output of the feature enhancement network by using the encoding-decoding structure to obtain rich building boundary features.In addition,in order to make the network more stable and effective,this paper combines the binary cross-entropy loss and the structure similarity loss,and supervises the training and learning of the model on both pixel and image structure levels.Through the test on the dataset WHU,in terms of objective metrics,the IoU and Precision of this network are improved compared with other classical algorithms,reaching 96.0% and 97.9% respectively.At the same time,in terms of subjective vision,the extracted building boundary is clearer and the region is more complete.

Key words: building extraction, boundary aware, encoding-decoding, remote sensing image, deep learning

CLC Number:

TP391

ZHANG Yan,WANG Xiangyu,ZHANG Zhongwei,SUN Yemei,LIU Shudong. Boundary-aware network for building extraction from remote sensing images[J].Journal of Xidian University, 2022, 49(1): 236-244.

Figures/Tables 9

References 28

[1]	YANG P, SONG W, ZHAO X B, et al. An Improved Otsu Threshold Segmentation Algorithm[J]. International Journal of Computational Science and Engineering, 2020, 22(1):146-153. doi: 10.1504/IJCSE.2020.107266
[2]	CHEN J S, HUERTAS A, MEDIONI G. Fast Convolution with Laplacian-of-Gaussian Masks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9 (4):584-590.
[3]	KANOPOULOS N, VASANTHAVADA N, BAKER R L. Design of an Image Edge Detection Filter Using the Sobel Operator[J]. IEEE Journal of Solid-State Circuits, 2002, 23(2):358-367. doi: 10.1109/4.996
[4]	黄亮, 姚丙秀, 陈朋弟, 等. 高分辨率遥感影像超像素的模糊聚类分割法[J]. 测绘学报, 2020, 49(5):589-597.
	HUANG Liang, YAO Bingxiu, CHEN Pengdi, et al. Superpixel Segmentation Method of High-Resolution Remote Sensing Image Based on Fuzzy Clustering[J]. Acta Geodaeticaet Cartographica Sinica, 2020, 49(5):589-597.
[5]	OK A O. Automated Detection of Buildings from Single VHR Multispectral Images Using Shadow Information and Graph Cuts[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 86(12):21-40. doi: 10.1016/j.isprsjprs.2013.09.004
[6]	LI M, ZANG S, ZHANG B, et al. A Review of Remote Sensing Image Classification Techniques:The Role of Spatio-Contextual Information[J]. European Journal of Remote Sensing, 2014, 47(1):389-411. doi: 10.5721/EuJRS20144723
[7]	SIRMACEK B, UNSALAN C. Building Detection from Aerial Images Using Invariant Color Features and Shadow Information[C]// 2008 23rd International Symposium on Computer and Information Sciences.Piscataway:IEEE, 2008:1-5.
[8]	ZHONG S, HUANG J, XIE W. A New Method of Building Detection from a Single Aerial Photograph[C]// 2008 9th International Conference on Signal Processing.Piscataway:IEEE, 2008:1219-1222.
[9]	ZhANG Y. Optimisation of Building Detection inSsatellite Images by Combining Multispectral Classification and Texture Filtering[J]. ISPRS Journal of Photogrammetry and Rremote Sensing, 1999, 54(1):50-60.
[10]	FERRAIOLI G. Multichannel InSAR Building Edge Detection[J]. IEEE Transactions on Geoence & Remote Sensing, 2010, 48(3):1224-1231.
[11]	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.
	LV Fenghua, SHU Ning, GONG Yan, 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.
[12]	DUNAEVA A V, KORNILOV F A. Specific Shape Building Detection from Aerial Imagery in Infrared Range[J]. Vestn.YuUrGU.Ser.Vych.Matem.Inform., 2017, 6(3):84-100.
[13]	吴炜, 骆剑承, 沈占锋, 等. 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[J]. 武汉大学学报:信息科学版, 2012, 37(7):800-805.
	WU Wei, LUO Jiancheng, SHEN Zhanfeng, et al. Building Extraction from High Resolution Remote Sensing Imagery Based on Spatial-Spectral Method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(7):800-805.
[14]	ZHONG C, XU Q, YANG F, et al. Building Change Detection for High-Resolution Remotely Sensed Images Based on a Semantic Dependency[C]// 2015 IEEE International Geoscience and Remote Sensing Symposium. Piscataway: IEEE, 2015:3345-3348.
[15]	崔艳鹏, 王元皓, 胡建伟. 一种改进YOLOv3的动态小目标检测方法[J]. 西安电子科技大学学报, 2020, 47(3):1-7.
	CUI Yanpeng, WANG Yuanhao, HU Jianwei. Detection Method for a Dynamic Small Target Using the Improved YOLOv3[J]. Journal of Xidian University, 2020, 47(3):1-7.
[16]	YANG X, YANG J, YAN J, et al. SCRDet:Towards More Robust Detection for Small, Cluttered and Rotated Objects[C]// 2019 IEEE/CVF International Conference on Computer Vision.Piscataway:IEEE, 2019:8231-8240.
[17]	TIAN Z, SHEN C, CHEN H, et al. FCOS:Fully Convolutional One-Stage Object Detection[C]// 2019 IEEE/CVF International Conference on Computer Vision.Piscataway:IEEE, 2019:9626-9635.
[18]	WANG G, WANG K, LIN L. Adaptively Connected Neural Networks[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2019:1781-1790.
[19]	BERTINETTO L, MUELLER R, TERTIKAS K, et al. Making Better Mistakes:Leveraging Class Hierarchies with Deep Networks[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2020:12503-12512.
[20]	ZORAN D, CHRZANOWSKI M, HUANG P S, et al. Towards Robust Image Classification Using Sequential Attention Models[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2020:9480-9489.
[21]	QIN X, ZHANG Z, HUANG C, et al. BASNet:Boundary-Aware Salient Object Detection[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2019:7471-7481.
[22]	CHEN Z, XU Q, CONG R, et al. Global Context-Aware Progressive Aggregation Network for Salient Object Detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7):10599-10606. doi: 10.1609/aaai.v34i07.6633
[23]	MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. Fully Convolutional Neural Networks for Remote Sensing Image Classification[C]// 2016 IEEE International Geoscience and Remote Sensing Symposium.Piscataway:IEEE, 2016:5071-5074.
[24]	YUAN J. Learning Building Extraction in Aerial Scenes with Convolutional Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(11):2793-2798. doi: 10.1109/TPAMI.2017.2750680
[25]	许慧敏. 基于深度学习U-Net模型的高分辨率遥感影像分类方法研究[D]. 成都: 西南交通大学, 2018.
[26]	WU G, SHAO X, GUO Z, et al. Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks[J]. Remote Sensing, 2018, 10(3):407. doi: 10.3390/rs10030407
[27]	季顺平, 魏世清. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J]. 测绘学报, 2019, 48(4):448-459.
	JI Shunping, WEI Shiqing. Building Extraction via Convolutional Neural Net Works from an Open Remote Sensing Building Dataset[J]. Acta Geodaeticaet Cartographica Sinica, 2019, 48(4):448-459.
[28]	张春森, 葛英伟, 蒋萧. 基于稀疏约束 SegNet 的高分辨率遥感影像建筑物提取[J]. 西安科技大学学报, 2020, 40(3):441-448.
	ZHANG Chunsen, GE Yingwei, JIANG Xiao. High-Resolution Remote Sensing Image Building Extraction Based on Sparsely Constrained SegNet[J]. Journal of Xi'an University of Science and Technology, 2020, 40(3):441-448.

模型	网络结构			损失函数		评价指标
模型	Baseline	IAM	FEN	L_BCE	L_SSIM	IoU	准确率
1	√			√	√	0.925	0.958
2	√	√		√	√	0.942	0.968
3	√	√	√	√		0.948	0.970
4	√	√	√	√	√	0.960	0.979

实验	方法	IoU	准确率	召回率
1	U-Net	0.843	0.870	0.964
2	SegNet	0.840	0.865	0.895
3	MC-FCN	0.854	0.880	0.967
4	SU-Net	0.888	0.952	0.930
5	LSPNet	0.864	0.882	0.971
6	BANet(文中方法)	0.960	0.979	0.945

Boundary-aware network for building extraction from remote sensing images

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 28

Related Articles 15

Metrics

Comments

Recommended 0

[1]	GAO Jie,HUO Zhiyong. Algorithmfor image inpainting in generative adversarial networks based on gated convolution [J]. Journal of Xidian University, 2022, 49(1): 216-224.
[2]	LIU Jiawei,ZHANG Wenhui,KOU Xiaoli,LI Yanni. Harnessing adversarial examples via input denoising and hidden information restoring [J]. Journal of Xidian University, 2021, 48(6): 23-31.
[3]	DONG Ruchan,JIAO Licheng,ZHAO Jin,SHEN Weiyan. Application of the deep fusion mechanism in object detection of remote sensing images [J]. Journal of Xidian University, 2021, 48(5): 128-138.
[4]	LI Peng,FENG Cunqian,XU Xuguang,TANG Zixiang. Ballistic target fretting classification network based on Bayesian optimization [J]. Journal of Xidian University, 2021, 48(5): 139-148.
[5]	YAN Jia,CAO Yudong,REN Jiaxing,CHEN Donghao,LI Xiaohui. Deep asymmetric compression Hashing algorithm [J]. Journal of Xidian University, 2021, 48(5): 212-221.
[6]	WANG Junjun,SUN Yue,LI Ying. Cloud removal method for the remote sensing image based on the GAN [J]. Journal of Xidian University, 2021, 48(5): 23-29.
[7]	NING Yang,DU Jianchao,HAN Shuo,YANG Chuankai. Fire segmentation based on the improved DeeplabV3+ and the analytical method for fire development [J]. Journal of Xidian University, 2021, 48(5): 38-46.
[8]	ZHOU Peng,YANG Jun. Semantic segmentation of remote sensing images based on neural architecture search [J]. Journal of Xidian University, 2021, 48(5): 47-57.
[9]	QI Yanjun,KONG Yueping,WANG Jiajing,ZHU Xudong. Gait recognition method combining LSTM and CNN [J]. Journal of Xidian University, 2021, 48(5): 78-85.
[10]	ZHANG Yuhao,CHENG Peitao,ZHANG Shuhao,WANG Xiumei. Lightweight image super-resolution with the adaptive weight learning network [J]. Journal of Xidian University, 2021, 48(5): 15-22.
[11]	SONG Jianfeng,MIAO Qiguang,WANG Chongxiao,XU Hao,YANG Jin. Multi-scale single object tracking based on the attention mechanism [J]. Journal of Xidian University, 2021, 48(5): 110-116.
[12]	HUI Haisheng,ZHANG Xueying,WU Zelin,LI Fenglian. Method for stroke lesion segmentation using the primary-auxiliary path attention compensation network [J]. Journal of Xidian University, 2021, 48(4): 200-208.
[13]	SUN Haojie,LI Miaoyu,ZHANG Panpan,XU Pengfei. Self-supervised facial asymmetry learning for automatic evaluation of facial paralysis [J]. Journal of Xidian University, 2021, 48(3): 115-122.
[14]	ZHANG Hua,GAO Haoran,YANG Xingguo,LI Wenmin,GAO Fei,WEN Qiaoyan. TargetedFool:an algorithm for achieving targeted attacks [J]. Journal of Xidian University, 2021, 48(1): 149-159.
[15]	YANG Hongyu,ZENG Renyun. Method for assessment of network security situation with deep learning [J]. Journal of Xidian University, 2021, 48(1): 183-190.