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：用于图像分类的深度学习模型通常基于预定义的图像类别在“封闭世界”假设下进行训练。然而，当模型部署时，可能会遇到不属于训练期间所遇类别的输入图像。这种场景在遥感图像分类中十分常见，因为图像可能来自不同地理区域、传感器和成像条件。本文处理的是检测与训练数据分布不同的遥感图像（即分布外图像）的问题。我们提出了一个用于遥感场景分类中分布外检测的基准，并评估了基于最大softmax概率和最近邻的检测器。实验结果表明，基于最近邻的方法具有显著的优势。