Cloud Detectionof ZY-3 Remote Sensing Images Based on Fully Convolutional Neural Network and Conditional Random Field

2019 ◽  
Vol 56 (10) ◽  
pp. 102802
Author(s):  
裴亮 Pei Liang ◽  
刘阳 Liu Yang ◽  
高琳 Gao Lin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Conditional Random Field ◽  
Remote Sensing Images

scholarly journals Erratum: Wang, S., et al. Improved Winter Wheat Spatial Distribution Extraction Using a Convolutional Neural Network and Partly Connected Conditional Random Field. Remote Sensing 2020, 12, 821

2020 ◽  
Vol 12 (10) ◽  
pp. 1568
Author(s):  
Shouyi Wang ◽  
Zhigang Xu ◽  
Chengming Zhang ◽  
Yuanyuan Wang ◽  
Shuai Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Winter Wheat ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Conditional Random Field

After re-considering the contribution of Jinghan Zhang, Zhongshan Mu, and Tianyu Zhao, respectively, we wish to remove them from the authorship of our paper [...]

scholarly journals Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation

2020 ◽  
Vol 12 (4) ◽  
pp. 625 ◽  
Author(s):  
Yantong Chen ◽  
Yuyang Li ◽  
Junsheng Wang ◽  
Weinan Chen ◽  
Xianzhong Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Random Field ◽  
Conditional Random Field ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Deep Convolution Neural Network ◽  
End To End

Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field. Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input. Using the Gaussian pairwise potential method and mean field approximation theorem, a conditional random field is established as the output of the recurrent neural network, thus achieving end-to-end connection. We compared the proposed method with other state-of-the-art methods on the dataset established by Google Earth and NWPU-RESISC45. Experiments show that the target detection accuracy of the proposed method and the ability of capturing fine details of images are improved. The mean intersection over union is 83.2% compared with other models, which indicates obvious advantages. The proposed method is fast enough to meet the needs for ship detection in remote sensing images.

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