基于语义指导和自适应卷积的遥感云检测算法

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

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 thin-clouds.