Deep learning models for image classification are typically trained under the "closed-world" assumption with a predefined set of image classes. However, when the models are deployed they may be faced with input images not belonging to the classes encountered during training. This type of scenario is common in remote sensing image classification where images come from different geographic areas, sensors, and imaging conditions. In this paper we deal with the problem of detecting remote sensing images coming from a different distribution compared to the training data - out of distribution images. We propose a benchmark for out of distribution detection in remote sensing scene classification and evaluate detectors based on maximum softmax probability and nearest neighbors. The experimental results show convincing advantages of the method based on nearest neighbors.

翻译：深度学习模型通常在预定义的一组图像类下进行训练，这被称为“封闭世界”假设。然而，当这些模型部署时，他们可能会面临着属于训练期间未遇到过的图像类别。这种情况在遥感图像分类中很常见，因为图像来自不同地理区域，传感器和成像条件。在本文中，我们处理检测来自与训练数据不同分布的遥感图像——外部分布图像的问题。我们提出了一个遥感场景分类外部分布检测的基准测试，并评估了基于最大softmax概率和最近邻的检测器。实验结果显示，基于最近邻的方法具有令人信服的优势。