LAUSR

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.