MDAFormer: Multi-level difference aggregation transformer for change detection of VHR optical imagery

[1]  K. Yin,et al.  MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images , 2022, Remote. Sens..

[2]  Yang Xu,et al.  A Two-Stage Seismic Damage Assessment Method for Small, Dense, and Imbalanced Buildings in Remote Sensing Images , 2022, Remote. Sens..

[3]  Min Xia,et al.  SUACDNet: Attentional change detection network based on siamese U-shaped structure , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Zhuo Zheng,et al.  Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters , 2021 .

[5]  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.

[6]  Decheng Wang,et al.  ADS-Net: An Attention-Based deeply supervised network for remote sensing image change detection , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Huifang Li,et al.  A Combined Loss-Based Multiscale Fully Convolutional Network for High-Resolution Remote Sensing Image Change Detection , 2021, IEEE Geoscience and Remote Sensing Letters.

[8]  Liangpei Zhang,et al.  A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Anima Anandkumar,et al.  SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers , 2021, NeurIPS.

[10]  Xiang Li,et al.  Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[11]  Jinyuan Shao,et al.  SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images , 2021, IEEE Geoscience and Remote Sensing Letters.

[12]  Tao Xiang,et al.  Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[14]  Karen C. Seto,et al.  A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change , 2020 .

[15]  Nicolas Usunier,et al.  End-to-End Object Detection with Transformers , 2020, ECCV.

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

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

[18]  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.

[19]  Moyang Wang,et al.  A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images , 2020, Remote. Sens..

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

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

[22]  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.

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

[24]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[25]  Christopher J. Post,et al.  Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[26]  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.

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

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

[29]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Knut Conradsen,et al.  Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies , 1998 .

[31]  Yazhou Ren,et al.  TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Liejun Wang,et al.  SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[33]  D. Hou,et al.  An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Jie Chen,et al.  FODA: Building Change Detection in High-Resolution Remote Sensing Images Based on Feature–Output Space Dual-Alignment , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  Stephen Lin,et al.  Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[36]  Ruochen Liu,et al.  Deep Depthwise Separable Convolutional Network for Change Detection in Optical Aerial Images , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Francesca Bovolo,et al.  Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach , 2013, IEEE Transactions on Geoscience and Remote Sensing.

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