Salient object detection in hyperspectral images is of interest in various image processing and computer vision applications. Many studies considering spectral information have been developed, extracting only low-level features from a hyperspectral image. In this paper, a CNN-based salient object detection method in hyperspectral imagery data is proposed to simultaneously exploit spatial and spectral information.

The proposed methodology incorporates Extended Morphology (EMP) followed by a CNN to utilize the information from nearby pixels and high-level features simultaneously. We have evaluated the performance of the proposed approach on two independent datasets to verify the generalization ability, viz. 1) Hyperspectral Salient Object Detection Dataset (HS-SOD) and 2) Pavia University dataset. An extensive quantitative analysis of the results revealed that the proposed method significantly outperforms other state-of-the-art methods by approximately > 2% of AUC and F-measure and lower mean absolute error for both the datasets.