# Salient object detection on hyperspectral images using features learned from unsupervised segmentation task

## TL;DR

This paper introduces a novel hyperspectral salient object detection method that leverages self-supervised CNN features from unsupervised segmentation, outperforming existing models in accuracy.

## Contribution

It proposes a new hyperspectral saliency detection approach combining manifold ranking with self-supervised CNN features from unsupervised segmentation.

## Key findings

- Outperforms state-of-the-art hyperspectral saliency models
- Uses self-supervised CNN features for high-level representation
- Demonstrates effectiveness on hyperspectral image datasets

## Abstract

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.