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.

提出一种基于像素描述符的异常分割模型 (ASD)，包括数据论证以及多级、多尺度的特征提取策略，应用于高空间分辨率遥感图像的异常分割。