BUILDING CHANGE DETECTION BY W-SHAPE RESUNET plus plus NETWORK WITH TRIPLE ATTENTION MECHANISM

被引:0
作者
Eftekhari, A. [1 ]
Samadzadegan, F. [1 ]
Javan, F. Dadrass [1 ,2 ]
机构
[1] Univ Tehran, Sch Surveying & Geospatial Engn, Coll Engn, Tehran 1439957131, Iran
[2] Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7522 NB Enschede, Netherlands
来源
ISPRS GEOSPATIAL CONFERENCE 2022, JOINT 6TH SENSORS AND MODELS IN PHOTOGRAMMETRY AND REMOTE SENSING, SMPR/ 4TH GEOSPATIAL INFORMATION RESEARCH, GIRESEARCH CONFERENCES, VOL. 48-4 | 2023年
关键词
Remote Sensing Image Change Detection; Deep Learning; Attention Mechanism; W-shape Networks; High-resolution Images; Dual Loss Function;
D O I
10.5194/isprs-archives-XLVIII-4-W2-2022-23-2023
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Building change detection in high resolution remote sensing images is one of the most important and applied topics in urban management and urban planning. Different environmental illumination conditions and registration problem are the most error resource in the bitemporal images that will cause pseudochanges in results. On the other hand, the use of deep learning technologies especially convolutional neural networks (CNNs) has been successful and considered, but usually causes the loss of shape and detail at the edges. Accordingly, we propose a W-shape ResUnet++ network in which images with different environmental conditions enter the network independently. ResUnet++ is a network with residual blocks, triple attention blocks and Atrous Spatial Pyramidal Pooling. ResUnet++ is used on both sides of the network to extract deeper and discriminator features. This improves the channel and spatial inter-dependencies, while at the same time reducing the computational cost. After that, the Euclidean distance between the features is computed and the deconvolution is done. Also, a dual loss function is designed that used the weighted binary cross entropy to solve the unbalance between the changed and unchanged data in change detection training data and in the second part, we used the mask-boundary consistency constraints that the condition of converging the edges of the training data and the predicted edge in the loss function has been added. We implemented the proposed method on two remote sensing datasets and then compared the results with state-of-the-art methods. The F1 score improved 1.52 % and 4.22 % by using the proposed model in the first and second dataset, respectively.
引用
收藏
页码:23 / 29
页数:7
相关论文
共 19 条
[1]   A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection [J].
Chen, Hao ;
Shi, Zhenwei .
REMOTE SENSING, 2020, 12 (10)
[2]   DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images [J].
Chen, Jie ;
Yuan, Ziyang ;
Peng, Jian ;
Chen, Li ;
Huang, Haozhe ;
Zhu, Jiawei ;
Liu, Yu ;
Li, Haifeng .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 :1194-1206
[3]   DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J].
Chen, Liang-Chieh ;
Papandreou, George ;
Kokkinos, Iasonas ;
Murphy, Kevin ;
Yuille, Alan L. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) :834-848
[4]  
Daudt RC, 2018, IEEE IMAGE PROC, P4063, DOI 10.1109/ICIP.2018.8451652
[5]   ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data [J].
Diakogiannis, Foivos, I ;
Waldner, Francois ;
Caccetta, Peter ;
Wu, Chen .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 162 (162) :94-114
[6]   DSA-Net: A novel deeply supervised attention-guided network for building change detection in high-resolution remote sensing images [J].
Ding, Qing ;
Shao, Zhenfeng ;
Huang, Xiao ;
Altan, Orhan .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 105
[7]   Attention mechanisms in computer vision: A survey [J].
Guo, Meng-Hao ;
Xu, Tian-Xing ;
Liu, Jiang-Jiang ;
Liu, Zheng-Ning ;
Jiang, Peng-Tao ;
Mu, Tai-Jiang ;
Zhang, Song-Hai ;
Martin, Ralph R. ;
Cheng, Ming-Ming ;
Hu, Shi-Min .
COMPUTATIONAL VISUAL MEDIA, 2022, 8 (03) :331-368
[8]   ResUNet plus plus : An Advanced Architecture for Medical Image Segmentation [J].
Jha, Debesh ;
Smedsrud, Pia H. ;
Riegler, Michael A. ;
Johansen, Dag ;
de Lange, Thomas ;
Halvorsen, Pal ;
Johansen, Havard D. .
2019 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2019), 2019, :225-230
[9]   Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set [J].
Ji, Shunping ;
Wei, Shiqing ;
Lu, Meng .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (01) :574-586
[10]   Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis [J].
Khelifi, Lazhar ;
Mignotte, Max .
IEEE ACCESS, 2020, 8 :126385-126400