Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet.

地理信息（例如海拔，纬度和经度）是常见的，但在遥感影像产品中是基本的元记录。在本文中，表明了这样一组记录为遥感影像中的云和雪检测提供了重要的先验。直觉来自一些共同的地理知识，其中许多知识很重要，但常常被忽视。例如，众所周知，在低纬度或低纬度地区降雪的可能性较小，并且不同地理区域的云朵可能具有各种视觉外观。先前的云雪探测方法只是忽略了此类信息的使用，而仅基于图像数据（波段反射率）进行探测。由于忽略了这些先验，这些方法中的大多数很难在复杂的场景（例如，云雪共存）中获得令人满意的性能。本文提出了一种新的神经网络，称为“地理信息驱动网络（GeoInfoNet）”，用于云和雪的检测。除了使用图像数据外，该模型还在训练和检测阶段集成了地理信息。专门设计了“地理信息编码器”，它将图像的高度，纬度和经度编码为一组辅助地图，然后将其馈送到检测网络。可以以端到端的方式训练提出的网络，并提取和融合密集的鲁棒特征。建立了一个新的名为“ Levir_CS”的云和雪检测数据集，其中包含4,168个高分1号卫星图像和相应的地理记录，并且比该字段中的其他数据集大20倍以上。实验表明，该方法在“ Levir_CS”上达到了云联合的90.74％和雪联合的78.26％。它大大超越了其他现有技术的云和雪检测方法。特征可视化还表明，该方法学习了一些与常识相近的重要先验。提议的数据集和GeoInfoNet的代码可在https://github.com/permanentCH5/GeoInfoNet中找到。特征可视化还表明，该方法学习了一些与常识相近的重要先验。提议的数据集和GeoInfoNet的代码可在https://github.com/permanentCH5/GeoInfoNet中找到。特征可视化还表明，该方法学习了一些与常识相近的重要先验。提议的数据集和GeoInfoNet的代码可在https://github.com/permanentCH5/GeoInfoNet中找到。