Abstract
Cloud detection is of great significance for the subsequent analysis and application of remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In this article, we propose a cloud detection method using convolutional neural networks based on cascaded feature attention and channel attention (CFCA-Net). The CFCA-Net uses cascaded feature attention module (CFAM) to enhance the attention of the network toward important color feature and texture feature. The CFAM cascaded the color feature attention and texture feature attention module in the encoder. The CFAN-Net also uses channel attention to highlight the important information in the channel dimensions. The attention module is based on multi-scale features and uses dilated convolution with different dilation rates to obtain information about multiple receptive fields. Moreover, a loss function combined quadtree and binary cross-entropy (BCE) was also introduced to make the network focus on the edge of cloud area. We validated our CFCA-Net on the Gaofen-1 wide field-of-view (WFV) imagery dataset. The experimental results show that the CFCA-Net performs well under different scenarios, and its overall accuracy reaches 97.55%. Moreover, subjective cloud detection results also prove the effectiveness of our algorithm.
Highlights
W ITH the development of satellite remote sensing technology, a large number of high-resolution remote sensing images have been used widely in marine pollution monitoring, urban planning, agricultural monitoring, and other fields
We have proposed a network for cloud detection, which contains cascaded feature attention module and channel attention module, named CFCA-Net
In order to verify the performance of cascaded feature attention module, we cascaded the color feature attention module and texture feature attention module to form the final cascaded feature attention network(CFAN).In order to verify the effectiveness of color feature extraction, we added color feature attention to the encoder and the attention weight was added to the coding network through concatenate in channel dimension
Summary
W ITH the development of satellite remote sensing technology, a large number of high-resolution remote sensing images have been used widely in marine pollution monitoring, urban planning, agricultural monitoring, and other fields. Xie et al [22] performed super-pixel segmentation on the remote sensing image to be detected, used a convolutional neural network to extract multi-scale features from the super-pixel, and divided the pixels into cloud pixels and non-cloud pixels. The effective extraction and utilization of these features can often improve the performance of cloud detection It has achieved good detection results on public dataset, and has a lighter network structure compared with ADUI-Net. We have proposed a network for cloud detection, which contains cascaded feature attention module and channel attention module, named CFCA-Net. The CFCA-Net is built on the encoder-decoder structure. (a) We designed a cascaded feature attention module to enhance the useful spatial information of the multi-scale feature maps and suppress invalid information This module extracts color features and texture features giving better results in remote sensing images with fewer bands. The similar points in the entire large area are replaced with single values, which reduce the proportion of the simple samples in the loss function compared to using all points to iterate the loss function
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