In the remote sensing image processing field, cloud and snow detection for high-resolution sensors and cloud and snow morphology in different latitudes of the world is challenging. A deep learning training model (Softmax) was developed to improve the accuracy of cloud and snow identification from Gaofen-1 and Pakistan Remote Sensing Satellite-1 images. First, more than 1800 scenes remote sensing images in various regions over the world are collected. Next, the texture details and spectral information of the objects are extracted. Finally, the Softmax model is applied to process the features to obtain the final cloud and snow masks. The cloud and snow detection results are evaluated by performing statistical analysis. The overall accuracy for cloud detection reaches 92.64% (kappa coefficient = 0.83) and for snow detection reaches 93.94% (kappa coefficient = 0.8). The algorithm is not only accurate but also computationally efficient. It is of great importance for image processing in ground segment and corresponding applications.

中文翻译：

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GF-1和PRSS-1遥感影像自动云雪检测

在遥感图像处理领域，高分辨率传感器的云雪检测和世界不同纬度的云雪形态具有挑战性。开发了深度学习训练模型（Softmax），以提高高分一号和巴基斯坦遥感卫星一号图像的云雪识别精度。一是采集了全球各地区1800多幅场景遥感影像。接下来，提取对象的纹理细节和光谱信息。最后应用Softmax模型对特征进行处理，得到最终的云和雪掩膜。云雪检测结果通过统计分析进行评估。云探测的整体准确度达到92.64％（kappa系数= 0.83），雪探测的整体准确度达到93。94%（kappa 系数 = 0.8）。该算法不仅准确，而且计算效率高。它对地面段的图像处理及相应应用具有重要意义。