  Various saliency detection algorithms from color images have been proposed to
mimic eye fixation or attentive object detection response of human observers
for the same scenes. However, developments on hyperspectral imaging systems
enable us to obtain redundant spectral information of the observed scenes from
the reflected light source from objects. A few studies using low-level features
on hyperspectral images demonstrated that salient object detection can be
achieved. In this work, we proposed a salient object detection model on
hyperspectral images by applying manifold ranking (MR) on self-supervised
Convolutional Neural Network (CNN) features (high-level features) from
unsupervised image segmentation task. Self-supervision of CNN continues until
clustering loss or saliency maps converges to a defined error between each
iteration. Finally, saliency estimations is done as the saliency map at last
iteration when the self-supervision procedure terminates with convergence.
Experimental evaluations demonstrated that proposed saliency detection
algorithm on hyperspectral images is outperforming state-of-the-arts
hyperspectral saliency models including the original MR based saliency model.
