CNN-Based Salient Object Detection on Hyperspectral Images Using Extended Morphology

Salient object detection in hyperspectral images (HSIs) 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 HSI. This letter proposes a convolutional neural network (CNN) based salient object detection method using hyperspectral imagery to utilize spatial and spectral information simultaneously. The proposed methodology incorporates an extended morphological profile (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 (PU) 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 the area under receiver operating characteristic (ROC) curve (AUC) and F-measure and lower mean absolute error for both datasets.