Study on Road Extraction Method in Remote Sensing Image

2012 ◽  
Author(s):  
Wang Xia ◽  
Tang Hongmei ◽  
Yang Yang ◽  
Li Yuan
Keyword(s):  
Remote Sensing ◽  
Extraction Method ◽  
Remote Sensing Image ◽  
Road Extraction

scholarly journals Remote Sensing Image Building Extraction Method Based on Deep Learning

2020 ◽  
Vol 1631 ◽  
pp. 012010
Author(s):  
Minshui Wang ◽  
Mingchang Wang ◽  
Guodong Yang ◽  
Ziwei Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Extraction Method ◽  
Remote Sensing Image ◽  
Building Extraction ◽  
Image Building

A Fast Algorithm for Road Recognition in Remote Sensing Image

2010 ◽  
Vol 108-111 ◽  
pp. 1344-1347
Author(s):  
Li Li Li ◽  
Yong Xin Liu
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Fast Algorithm ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Feature Integration ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Simulation Results

In general, the road extraction methods in remote sensing images mainly are edge detection, feature integration, and so on. A fast road recognition arithmetic is presented in this paper. First using adaptive binarization arithmetic, the path on remote sensing images is extracted. Then morphological method is used to process image. Finally, the extracted image superimposed with the original and get clear road. Simulation results shows that this algorithm is efficiency, the anti-noise ability is enhance, and more precision.

scholarly journals Adaptive Filtering Remote Sensing Image Segmentation Network based on Attention Mechanism

2021 ◽  
Author(s):  
Cong zhong Wu ◽  
Hao Dong ◽  
Xuan jie Lin ◽  
Han tong Jiang ◽  
Li quan Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Adaptive Filtering ◽  
High Frequency ◽  
Remote Sensing Image ◽  
Aerial Image ◽  
Data Sets ◽  
Road Extraction ◽  
Image Labeling ◽  
Dilated Convolution ◽  
Segmentation Accuracy

It is difficult to segment small objects and the edge of the object because of larger-scale variation, larger intra-class variance of background and foreground-background imbalance in the remote sensing imagery. In convolutional neural networks, high frequency signals may degenerate into completely different ones after downsampling. We define this phenomenon as aliasing. Meanwhile, although dilated convolution can expand the receptive field of feature map, a much more complex background can cause serious alarms. To alleviate the above problems, we propose an attention-based mechanism adaptive filtered segmentation network. Experimental results on the Deepglobe Road Extraction dataset and Inria Aerial Image Labeling dataset showed that our method can effectively improve the segmentation accuracy. The F1 value on the two data sets reached 82.67% and 85.71% respectively.

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