Lone, Z.A.Pais, A.R.2026-02-062024Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, Vol.13102 LNCS, , p. 451-4583029743https://doi.org/10.1007/978-3-031-12700-7_46https://idr.nitk.ac.in/handle/123456789/29005Salient object detection has been explored extensively in low dimensional images like RGB, grayscale, etc., but have been explored very little in high dimensional images like Hyperspectral images (HSI) etc. In HSI, few studies have used low-level features to perform salient object detection. In this paper, we propose a high-level feature-based salient object detection algorithm. The manifold ranking is applied on the self-supervised CNN features learned by an unsupervised segmentation task. The training of the model continues until the clustering loss or saliency map converges to a defined error. We found out that the proposed algorithm performed better than state-of-the-art in terms of precision. © Springer Nature Switzerland AG 2024.CNNHyperspectral imagesSalient object detection