Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual ab-normal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications.

翻译：高空间分辨率遥感图像中的异常分割旨在分割偏离正常模式的地球异常图案，这在各种地球视觉应用中发挥着重要作用。然而，由于目标分布复杂、形状不规则且缺少异常样本，该任务具有挑战性。为解决这些问题，提出了一种基于像素描述符的异常分割模型（ASD），用于高空间分辨率遥感图像的异常分割。具体而言，引入深度单类分类，在具有判别性像素描述符的特征空间中实现异常分割。ASD模型结合了数据增强技术以生成虚拟异常样本，这可以迫使像素描述符对正常数据保持紧凑，同时保持多样性，以避免仅使用正样本训练时的模型坍缩问题。此外，ASD引入了多层级、多尺度特征提取策略，用于学习低层与语义信息，使像素描述符特征丰富。所提出的ASD模型在四个高分辨率数据集上进行了验证，并与当前最先进模型进行了比较，展示了其在地球视觉应用中的潜在价值。