Classification of very high-resolution remote sensing images by applying a new edge-based marker-controlled watershed segmentation method

2019 ◽  
Vol 13 (7) ◽  
pp. 1319-1327 ◽  
Author(s):  
Nafiseh Kakhani ◽  
Mehdi Mokhtarzade ◽  
Muhammad Javad Valadan Zouj
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Remote Sensing Images ◽  
Edge Based ◽  
Very High

scholarly journals A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

2016 ◽  
Vol 8 (10) ◽  
pp. 814 ◽  
Author(s):  
ZhiYong Lv ◽  
Haiqing He ◽  
Jón Benediktsson ◽  
Hong Huang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Supervised Classification ◽  
Remote Sensing Imagery ◽  
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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