基于语义指导和自适应卷积的遥感云检测算法

Cloud detection algorithm for remote sensing images based on semantic-guided and adaptive convolution

遥感卫星数据云检测分割是遥感影像处理中的重要环节,为了解决目前碎云薄云检测精度较低的问题,提出了一种采用基于高阶语义解码和自适应卷积编码的云检测方法。这种方法针对云团和碎云薄云之间的空间分布联系,提出了自适应卷积编码器来提取云团之间的关联信息。然后,使用高阶语义指导模块来解码语义特征,指导高分辨率的云掩码图生成。此外,这种方法还设计了一种动态联合损失函数,该损失函数通过动态计算样本中的漏检误检像素来构建权重,以引导神经网络关注碎云薄云特征,从而提高整体精度。实验结果表明,提出的算法在遥感图像上云分割能力可以达到96.5%的精确度和88.1%的交并比,可以很好地检测碎云薄云。

Cloud detection of remote sensing satellite data is a crucial component in the processing of remote sensing images.To address the issue of low accuracy in detecting broken-clouds and thin-clouds,this paper proposes a novel cloud detection method that utilizes high-order semantic-guided decoding and adaptive convolutional encoding.The method leverages the spatial distribution relationship between the main cloud and broken-clouds by introducing an adaptive convolutional encoder to extract correlation information between the main cloud clusters.A high-order semantic-guided decoding module is then utilized to decode semantic features,thus restoring high-resolution cloud mask images.Moreover,a dynamic fusion loss function is designed to calculate the weight by dynamically computing the missed and wrong pixels in the prediction,guiding the network to focus on broken-clouds and thin-clouds,features,thereby enhancing the overall accuracy.Experimental results demonstrate that the proposed algorithm achieves an accuracy of over 96.5% and an intersection over union of over 88.1%,effectively detecting broken-clouds and thinclouds.