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Cloud Coverage Estimation Network for Remote Sensing Images
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PRE
AI
DOI：10.1109/LGRS.2022.3220266.png)
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摘要
En 中文
The main purpose of cloud detection is to estimate cloud coverage and thus determine whether to transmit remote sensing images to earth or execute subsequent tasks based on cloud coverage. Fast and accurate cloud coverage estimation is a necessary preprocessing step on board. Therefore, we propose a new approach for cloud coverage estimation using a regression network to directly predict the coverage. A cloud coverage estimation network, which is termed (CE)-E-2-Net, is proposed in this work. The proposed network consists of three modules, including an encoder for representation feature extraction, a coverage estimation for predicting the cover rate of clouds, and an auxiliary supervision module for improving the performance of the model. To verify the effectiveness of our method, experiments are performed on two open-source datasets (Landset 8 Biome dataset and GaoFen-1 WFV dataset). Our method effectively improves the efficiency of cloud detection by at least doubling, while keeping the estimation error low.
Keyword:
Estimation
Remote sensing
Feature extraction
Earth
Image segmentation
Convolution
Artificial satellites
Cloud coverage estimation
convolutional neural network
regression network
remote sensing images (RSIs)
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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