Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
This work addresses salient object detection for hyperspectral imaging, which is incremental as it builds on existing methods by incorporating high-level features.
The paper tackled salient object detection on hyperspectral images by proposing a model that uses manifold ranking on self-supervised CNN features from an unsupervised segmentation task, achieving state-of-the-art performance and outperforming existing hyperspectral saliency models.
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.