Hyperspectral Salient Object Detection Using Extended Morphology with CNN

Salient object detection using hyperspectral images is crucial for various image processing and computer vision applications. Many studies considering spectral information have been developed, extracting only low-level features from a hy-perspectral image. In this research work, a dataset specifically developed for salient object detection called HS-SOD is considered exploiting both spatial and spectral information equally. To include spatial information, Extended Morpho-logical Profile (EMP) has been considered. EMP incorpo-rates spatial characteristics by including nearby pixel information. A convolution neural network (CNN) is integrated with extended morphology to extract high-level features. It detect objects of multiple spatial scales and ratios, preserving boundary edges. We observed an improvement of 5 % in overall accuracy while using EMP with CNN compared to that of using EMP without CNN. Thus, the experimental re-sults demonstrate the effectiveness of EMP with CNN on the hyperspectral datasets.