Residual Dual U-Shape Networks With Improved Skip Connections for Cloud Detection
Ao Li, Xinghua Li, Xiaoshuang Ma
Abstract
Cloud detection in remote sensing images is a challenging task that plays a crucial role in various applications. A novel residual dual U-shape network (RD-UNet) is proposed for cloud detection. The primary innovation lies in that it cascades two U-shaped networks and leverages a residual-like connection to enhance information flow between two networks, thus optimizing details and edge information. Moreover, another contribution is that the improved skip connections (ISCs) efficiently facilitate multiscale feature utilization, aiding in the identification of thin clouds and distinguishing other confounding land features. The effectiveness of RD-UNet was demonstrated through extensive experiments on two public datasets, outperforming state-of-the-art methods with a nearly 2% improvement in F1 score and superior visual effect for multispectral images. The code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lixinghua5540/RD-UNet</uri> .