Fast and accurate denoising method applied to very high resolution optical remote sensing images

2017 ◽  
Author(s):  
Antoine Masse ◽  
Sebastien Lefevre ◽  
Renaud Binet ◽  
Gwendoline Blanchet ◽  
Stéphanie Artigues ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Denoising Method ◽  
Very High

scholarly journals Denoising Very High Resolution Optical Remote Sensing Images: Application and Optimization of Nonlocal Bayes method

2018 ◽  
Vol 11 (3) ◽  
pp. 691-700 ◽  
Author(s):  
Antoine Masse ◽  
Sebastien Lefevre ◽  
Renaud Binet ◽  
Stephanie Artigues ◽  
Gwendoline Blanchet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Bayes Method ◽  
Very High

scholarly journals Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model

2020 ◽  
Vol 12 (18) ◽  
pp. 2985 ◽  
Author(s):  
Yeneng Lin ◽  
Dongyun Xu ◽  
Nan Wang ◽  
Zhou Shi ◽  
Qiuxiao Chen
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Network Models ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Proposed Model ◽  
Wide Range ◽  
Network Modules ◽  
Very High

Automatic road extraction from very-high-resolution remote sensing images has become a popular topic in a wide range of fields. Convolutional neural networks are often used for this purpose. However, many network models do not achieve satisfactory extraction results because of the elongated nature and varying sizes of roads in images. To improve the accuracy of road extraction, this paper proposes a deep learning model based on the structure of Deeplab v3. It incorporates squeeze-and-excitation (SE) module to apply weights to different feature channels, and performs multi-scale upsampling to preserve and fuse shallow and deep information. To solve the problems associated with unbalanced road samples in images, different loss functions and backbone network modules are tested in the model’s training process. Compared with cross entropy, dice loss can improve the performance of the model during training and prediction. The SE module is superior to ResNext and ResNet in improving the integrity of the extracted roads. Experimental results obtained using the Massachusetts Roads Dataset show that the proposed model (Nested SE-Deeplab) improves F1-Score by 2.4% and Intersection over Union by 2.0% compared with FC-DenseNet. The proposed model also achieves better segmentation accuracy in road extraction compared with other mainstream deep-learning models including Deeplab v3, SegNet, and UNet.

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