Road Extraction from High-Resolution Remote Sensing Images Based on Synthetical Characteristics

Chen, Yong Sheng; Hong, Zhi; He, Qun; Bin, Hong

doi:10.4028/www.scientific.net/amm.333-335.828

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Recent research has developed deep-learning methods for road detection, and deep belief networks have been applied for the first time to the task of extracting roads from airborne remote sensing images [37]. Considering the particularity of remote sensing images, Yong et al [38] built a model based on texture features and other auxiliary feature information to realize automatic road extraction from VHR remote sensing images [38]. Deep residual UNet has been proposed to simplify the training of a deep network model, and it can extract road features from remote sensing images with less parameters compared with original UNet model [39].…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Wang

et al. 2020

Remote Sensing

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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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Section: Introductionmentioning

confidence: 99%

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

Lin

Wang

et al. 2020

Remote Sensing

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DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution

et al. 2022

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Recent advances in deep learning have shown remarkable performance in road segmentation from remotely sensed images. However, these methods based on convolutional neural networks (CNNs) cannot obtain long-range dependency and global contextual information because of the intrinsic inductive biases. Motivated by the success of Transformer in computer vision (CV), excellent models based on Transformer are emerging endlessly. However, patches with a fixed scale limit the further improvement of the model performance. To address this problem, a dual-resolution road segmentation network (DCS-TransUperNet) with a features fusion module (FFM) was proposed for road segmentation. Firstly, the encoder of DCS-TransUperNet was designed based on CSwin Transformer, which uses dual subnetwork encoders of different scales to obtain the coarse and fine-grained feature representations. Secondly, a new FFM was constructed to build enhanced feature representation with global dependencies, using different scale features from the subnetwork encoders. Thirdly, a mixed loss function was designed to avoid the local optimum caused by the imbalance between road and background pixels. Experiments using the Massachusetts dataset and DeepGlobe dataset showed that the proposed DCS-TransUperNet could effectively solve the discontinuity problem and preserve the integrity of the road segmentation results, achieving a higher IoU (65.36% on Massachusetts dataset and 56.74% on DeepGlobe) of road segmentation compared to other state-of-the-art methods. The considerable performance also proves the powerful generation ability of our method.

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Road Extraction from High-Resolution Remote Sensing Images Based on Synthetical Characteristics

Cited by 2 publications

References 4 publications

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

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

DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution

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