A high-fidelity method for thin cloud removal from remote sensing images based on attentional feature fusion
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摘要: 为改善非均匀薄云覆盖遥感图像薄云去除存在校正不足或颜色失真的问题,该文提出了一个融合注意力特征的高保真薄云去除端到端网络。首先,设计了注意力特征融合模块,该模块引入注意力机制以及融合模块,并通过3个注意力特征融合模块级联,使网络关注薄云覆盖区域的信息提取,减少无云部分的影响。同时,加入颜色损失函数和锐化损失函数以提高图像准确的颜色保真度和细节清晰度。通过实验结果表明,该方法的输出结果在视觉评估和定量评价指标(峰值信噪比和结构相似度)上均优于其他方法的结果。该网络对于多种场景下的非均匀薄云图像均有较好的去云效果,且输出的图像色彩效果真实、亮度过度平滑、细节轮廓清晰。Abstract: The thin cloud removal from remote sensing images with uneven thin cloud cover suffers from undercorrection or color distortion. This study proposed a high-fidelity end-to-end network method for thin cloud removal based on attentional feature fusion. First, this study designed an attentional feature fusion module integrating the attention mechanism and a fusion module. Through the cascade of three attentional feature fusion modules, the network focused on extracting the information on thin-cloud cover areas, reducing the impact of cloud-free areas. Furthermore, this study improved the color fidelity and detail clarity of images using the color and sharpening loss functions. The experimental results show that this method outperformed other methods in visual and quantitative evaluation indices (peak signal-to-noise ratio and structural similarity). This method yielded satisfactory effects of cloud removal in images with uneven thin cloud cover in various scenarios, producing images with actual colors, smooth brightness transition, and distinct detail contours.
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Key words:
- thin cloud removal /
- attention mechanism /
- feature fusion /
- remote sensing image /
- deep learning
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