CNN-Based Salient Object Detection on Hyperspectral Images Using Extended Morphology
Koushikey Chhapariya, Krishna Mohan Buddhiraju, Anil Kumar · IEEE Geoscience and Remote Sensing Letters · 2022
Salient object detection in hyperspectral images is of interest in various image processing and computer vision applications. Many studies considering spectral information have been reported, extracting only low-level features from a hyperspectral image. This paper proposes a Convolutional Neural Network (CNN) based salient object detection method using hyperspectral imagery to utilise spatial and spectral information simultaneously. The proposed methodology incorporates Extended Morphological Profile (EMP) followed by a CNN to utilise 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 generalisation 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 (Area Under receiver operating characteristic Curve) and F-measure and lower mean absolute error for both datasets.