Abstract
Object detection in hyperspectral images is an important task for many applications. While most traditional methods are pixel-based, many recent efforts have been put on extracting spatial-spectral features. In this paper, we introduce Itti's visual saliency model into the spectral domain for object detection. This enables the extraction of salient spectral features, which is related to the material property and spatial layout of objects, in the scale space. To our knowledge, this is the first attempt to combine hyperspectral data with salient object detection. Three methods have been implemented and compared to show how color component in the traditional saliency model can be replaced by spectral information. We have performed experiments on selected images from three online hyperspectral datasets, and show the effectiveness of the proposed methods.
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