Hyperspectral anomaly detection (HAD) is a branch of target detection which tries to locate pixels that are spectrally or spatially different from their background. In this paper, a visual attention approach is developed to leverage HAD. Traditional HAD methods often try to locate anomalous pixels based on spectral information. However, the spatial features of hyperspectral datasets provide valuable information. Here, we aim to fuse spatial and spectral anomaly features based on bottom-up (BU) and top-down (TD) visual attention mechanisms. Owe to the BU attention, spatial features are extracted by mimicking the primary visual cortex neurons functionality. Also, spectral information is obtained throughout a deep neural network that imitating the TD visual attention. The BU and TD approaches’ results are then integrated to provide both spectral and spatial information. The key findings of our results demonstrate the proposed method outperforms the six state-of-the-art AD methods based on different evaluation metrics.

高光谱异常检测（HAD）是目标检测的一个分支，旨在查找与背景在光谱或空间上不同的像素。在本文中，开发了一种视觉注意方法来利用HAD。传统的HAD方法经常尝试根据光谱信息来定位异常像素。但是，高光谱数据集的空间特征提供了有价值的信息。在这里，我们旨在融合基于自下而上（BU）和自上而下（TD）视觉注意机制的空间和频谱异常特征。敬请注意，通过模仿主要视觉皮层神经元功能来提取空间特征。而且，在整个深度神经网络中都获得了光谱信息，这些神经网络模仿了TD的视觉注意力。然后将BU和TD方法的结果整合在一起，以提供光谱和空间信息。我们结果的关键发现表明，基于不同的评估指标，该方法优于六种最新的AD方法。