CNN-based Salient Object Detection on Hyperspectral Images using Extended Morphology

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.