AGCDetNet:An Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images

While deep learning-based methods have gained considerable improvements in remote sensing (RS) image change detection (CD), scale variations and pseudochanges hinder most supervised methods’ performance. The CD networks derived from other fields can be fronted with false alarms and miss detections in high-resolution RS images due to the weak feature representation ability. In this article, an attention-guided end-to-end change detection network (AGCDetNet) is proposed based on the fully convolutional network and attention mechanism. AGCDetNet learns to enhance the feature representation of change information and achieve accuracy improvements using spatial attention and channel attention. A spatial attention module (SPAM) promotes the discrimination between the changed objects and the background by adding the learned spatial attention to the deep features. Channelwise attention-guided interference filtering unit (CIFU)/atrous spatial pyramid pooling (CG-ASPP) module enhances the representation of multilevel features and multiscale context, respectively. Extensive experiments have been conducted on several public datasets for performance evaluation, including LEVIR-CD, WHU, Season-Varying, WV2, and ZY3. Experiment results demonstrate that AGCDetNet outperforms several state-of-the-art methods of accuracy and robustness. Specifically, AGCDetNet achieves the best F1-score on two datasets, i.e., LEVIR-CD (0.9076) and Season-Varying (0.9654).

[1]  Wenzhong Shi,et al.  A Feature Difference Convolutional Neural Network-Based Change Detection Method , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Yunhong Wang,et al.  Change Detection Based on Deep Features and Low Rank , 2017, IEEE Geoscience and Remote Sensing Letters.

[3]  Francesca Bovolo,et al.  Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[5]  Francesca Bovolo,et al.  Supervised change detection in VHR images using contextual information and support vector machines , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[7]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[8]  Xiwen Yao,et al.  Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images , 2021, IEEE Geoscience and Remote Sensing Letters.

[9]  R. D. Johnson,et al.  Change vector analysis: A technique for the multispectral monitoring of land cover and condition , 1998 .

[10]  Nicholas J. Tate,et al.  A critical synthesis of remotely sensed optical image change detection techniques , 2015 .

[11]  Jinsong Deng,et al.  PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data , 2008 .

[12]  Li Chen,et al.  DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[14]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[15]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

[16]  Maoguo Gong,et al.  A Generative Discriminatory Classified Network for Change Detection in Multispectral Imagery , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Xiao Xiang Zhu,et al.  Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Yongjun Zhang,et al.  End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++ , 2019, Remote. Sens..

[19]  Ryosuke Nakamura,et al.  Damage detection from aerial images via convolutional neural networks , 2017, 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).

[20]  Nikos Komodakis,et al.  Learning to compare image patches via convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Gong Cheng,et al.  P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  D. Lu,et al.  Change detection techniques , 2004 .

[24]  Menglong Yan,et al.  Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection , 2019, IEEE Geoscience and Remote Sensing Letters.

[25]  Martino Pesaresi,et al.  Toward Global Automatic Built-Up Area Recognition Using Optical VHR Imagery , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Qingjie Liu,et al.  From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[27]  William J. Emery,et al.  Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Ruofei Zhong,et al.  Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Wenzhong Shi,et al.  Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges , 2020, Remote. Sens..

[31]  Abhinav Gupta,et al.  Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[32]  Xilin Chen,et al.  Object-Contextual Representations for Semantic Segmentation , 2019, ECCV.

[33]  Yury Vizilter,et al.  CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[34]  Xiangtao Zheng,et al.  Sound Active Attention Framework for Remote Sensing Image Captioning , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Yongjun Zhang,et al.  Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples , 2019, Remote. Sens..

[36]  Liangpei Zhang,et al.  Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Le Wang,et al.  Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Yanning Zhang,et al.  Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network , 2020, Sensors.

[39]  Maoguo Gong,et al.  A Conditional Adversarial Network for Change Detection in Heterogeneous Images , 2019, IEEE Geoscience and Remote Sensing Letters.

[40]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[41]  Hassiba Nemmour,et al.  Multiple support vector machines for land cover change detection: An application for mapping urban extensions , 2006 .

[42]  Alexandre Boulch,et al.  Multitask learning for large-scale semantic change detection , 2018, Comput. Vis. Image Underst..

[43]  Alexey Shvets,et al.  TernausNetV2: Fully Convolutional Network for Instance Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[44]  Zhuo Zheng,et al.  Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[46]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[47]  Maggi Kelly,et al.  Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition , 2016 .

[48]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[49]  Huseyin Kusetogullari,et al.  Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm , 2017, IEEE Geoscience and Remote Sensing Letters.

[50]  Dong-Chen He,et al.  Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge , 2010 .

[51]  Hao Chen,et al.  A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection , 2020, Remote. Sens..

[52]  Hong Huo,et al.  Dual Pyramid Attention Network for High-resolution Remotely Sensed Image Change Detection , 2020, ICMLC.

[53]  Peng Yue,et al.  A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images , 2020 .

[54]  Alexandre Boulch,et al.  Fully Convolutional Siamese Networks for Change Detection , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[55]  Gang Wang,et al.  Progressive Attention Guided Recurrent Network for Salient Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[56]  Thomas A. Funkhouser,et al.  Dilated Residual Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[57]  Lichao Mou,et al.  Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection , 2016, Remote. Sens..

[58]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[59]  Xiaoqiang Lu,et al.  Vision-to-Language Tasks Based on Attributes and Attention Mechanism , 2019, IEEE Transactions on Cybernetics.

[60]  Allan Aasbjerg Nielsen,et al.  The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data , 2007, IEEE Transactions on Image Processing.

[61]  Howie Choset,et al.  xBD: A Dataset for Assessing Building Damage from Satellite Imagery , 2019, ArXiv.

[62]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[63]  Turgay Çelik,et al.  Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.

[64]  Meng Lu,et al.  Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[65]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  Menglong Yan,et al.  Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[67]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[68]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[69]  Liangpei Zhang,et al.  Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[70]  Maoguo Gong,et al.  A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images , 2018, IEEE Transactions on Neural Networks and Learning Systems.