Optimizing Convolutional Neural Networks for Cloud Detection

2017 ◽  
Author(s):  
Travis Johnston ◽  
Steven R. Young ◽  
David Hughes ◽  
Robert M. Patton ◽  
Devin White
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cloud Detection

scholarly journals SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images

2021 ◽  
Vol 13 (15) ◽  
pp. 2910
Author(s):  
Xiaolong Li ◽  
Hong Zheng ◽  
Chuanzhao Han ◽  
Wentao Zheng ◽  
Hao Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Method ◽  
Detection Methods ◽  
Utilization Rate ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Deep Convolutional Neural Networks

Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.

scholarly journals Cloud detection using convolutional neural networks on remote sensing images

Solar Energy ◽  
2021 ◽  
Vol 230 ◽  
pp. 1020-1032
Author(s):  
Lysha M. Matsunobu ◽  
Hugo T.C. Pedro ◽  
Carlos F.M. Coimbra
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cloud Detection ◽  
Remote Sensing Images
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