A bottom-up and top-down human visual attention approach for hyperspectral anomaly detection

Abstract

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.