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Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network

Yi Zhang, Shizhou Zhang, Ying Li, Yanning Zhang
2020 Sensors  
To deal with these problems, we design a coarse-to-fine detection framework via a boundary-aware attentive network with a hybrid loss to detect the change in high resolution satellite images.  ...  Existing approaches based on deep learning frameworks have achieved good performance for the task of change detection on satellite images.  ...  To tackle the above problems, we design a coarse-to-fine change detection framework to process satellite image pairs with a boundary-aware attentive network (BA 2 Net), which utilizes the image-pairwise  ... 
doi:10.3390/s20236735 pmid:33255688 fatcat:ij46sxbcqnfxtixx6653n4p3ey

Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation [article]

Dino Ienco, Yawogan Jean Eudes Gbodjo, Roberto Interdonato, Raffaele Gaetano
2020 arXiv   pre-print
To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier  ...  One of the main task associated to SITS data analysis is related to land cover mapping where satellite data are exploited via learning methods to recover the Earth Surface status aka the corresponding  ...  In [26] , an object-based change detection approach of bitemporal SITS data is introduced.  ... 
arXiv:2004.14672v1 fatcat:kmniw5nj45djlhku4ntmp3tfa4

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Xu, Y., +, JSTARS 2020 72-88 Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes.  ...  ., +, JSTARS 2020 1986-1995 Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Wang, S., +, TIP 2020 2424-2438 Coarse-to-Fine Semantic Segmentation From Image-Level Labels.  ...  Wang, K., +, TIP 2020 3416-3428 Coarse-to-Fine Semantic Segmentation From Image-Level Labels.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

Yi Zhang, Lei Fu, Ying Li, Yanning Zhang
2021 Remote Sensing  
To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task.  ...  Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively  ...  [36] propose a coarse-to-fine changedetection framework via high-level features guided network to use context information to better locate more change areas.  ... 
doi:10.3390/rs13081440 fatcat:ox2oot42pjcybpgehvbwst56ei

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, Scene Independent End-to-End Spatiotemporal Feature Learning Framework for Change Detection in Unseen Videos.  ...  ., +, TIP 2021 7112-7126 Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L., and Hanssen, R.F., Incorporating Temporary  ...  Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS  ...  Xu, X., +, TGRS Dec. 2019 9612-9625 A Coarse-to-Fine Framework for Cloud Removal in Remote Sensing Image Sequence.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Siamese Attentional Cascade Keypoints Network for Visual Object Tracking

Ershen Wang, Donglei Wang, Yufeng Huang, Gang Tong, Song Xu, Tao Pang
2020 IEEE Access  
INDEX TERMS Visual object tracking, siamese network, hourglass network, global attention, cascade corner pooling.  ...  Compared to complex target prediction, the anchor-free method is performed to avoid plaguy hyperparameters, and a simplified hourglass network with global attention is considered the backbone to improve  ...  SPM-Tracker [32] combined coarse and fine matching to improve the robustness and power of discrimination.  ... 
doi:10.1109/access.2020.3046731 fatcat:bmglla6htnfi7lztzm736cd52e

Deep Learning and Earth Observation to Support the Sustainable Development Goals [article]

Claudio Persello, Jan Dirk Wegner, Ronny Hänsch, Devis Tuia, Pedram Ghamisi, Mila Koeva, Gustau Camps-Valls
2021 arXiv   pre-print
We systematically review case studies to 1) achieve zero hunger, 2) sustainable cities, 3) deliver tenure security, 4) mitigate and adapt to climate change, and 5) preserve biodiversity.  ...  New developments and a plethora of applications are already changing the way humanity will face the living planet challenges.  ...  In the context of EO applications, deep networks can address a large variety of analysis tasks, from image classification and segmentation to data fusion, change detection, object detection and delineation  ... 
arXiv:2112.11367v1 fatcat:7eve5dr45vcublfqyzzrccuvxa

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline [article]

Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu
2021 arXiv   pre-print
By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way.  ...  Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output.  ...  Adaptations of this method for SAR coastline detection use multiple level set iterations to go from coarse to fine delineations [55] or sophisticated preprocessing steps [56] to make the method work  ... 
arXiv:2103.01849v1 fatcat:xqbiar2oxjgmldj2efqygfdcbe

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Zijun Yang, Chunyuan Diao, Bo Li
2021 Remote Sensing  
SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images.  ...  Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes  ...  We are also grateful of the three reviewers' constructive comments to help improve this manuscript.  ... 
doi:10.3390/rs13245005 fatcat:hxskra4gijdohgzajqrchaduuu

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu
2021 IEEE Transactions on Geoscience and Remote Sensing  
By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way.  ...  Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output.  ...  Adaptations of this method for SAR coastline detection use multiple level set iterations to go from coarse to fine delineations [55] or sophisticated preprocessing steps [56] to make the method work  ... 
doi:10.1109/tgrs.2021.3064606 fatcat:65s7m2w7aja2xblcwmoydzcy3i

Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction

Lixian Zhang, Runmin Dong, Shuai Yuan, Weijia Li, Juepeng Zheng, Haohuan Fu
2021 Remote Sensing  
In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware  ...  In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction.  ...  Hereby, we propose the edge-aware super-resolved building segmentation network (SRBuildingSeg) as a novel framework to achieve super-resolution building extraction.  ... 
doi:10.3390/rs13152872 fatcat:vez7co4nuvcj3bpwvfwpojr3vy

Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images [article]

Libo Wang, Ce Zhang, Rui Li, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson
2021 arXiv   pre-print
To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for semantic segmentation of MSR remotely sensed imagery.  ...  However, MSR images suffer from two critical issues: 1) increased scale variation of geo-objects and 2) loss of detailed information at coarse spatial resolutions.  ...  Acknowledgements: The authors are very grateful to the many people who helped to comment on the article, and the Large Scale Environment Remote Sensing Platform (Facility No. 16000009, 16000011, 16000012  ... 
arXiv:2103.07935v4 fatcat:lyfg7cjcwzgg3huswrfc6q2jci

Challenges and Opportunities in Applying High-Fidelity Travel Demand Model for Improved Network-Wide Traffic Estimation: A Review and Discussion

Riad Mustafa, Ming Zhong
2014 The Open Transportation Journal  
It divides a study area into traffic analysis zones (TAZs) sometimes at a very coarse level, which are assumed to be spatially homogeneous and zonal attributes are mostly represented by aggregate averages  ...  Evidently, fairly large TAZs and limited roadway network (arterials and above) used in the traditional travel demand modeling framework caused biased and unbalanced trip distribution and assignment over  ...  For example, GIS can be used to achieve a multiple map layer overlay, which helps in detecting and identifying TAZ and census boundaries.  ... 
doi:10.2174/1874447801408010001 fatcat:az4jhn4bknhebkzmiwva3lht6i
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