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Author: search.filters.author.Lone, Z.A.Subject: search.filters.subject.Hyperspectral images

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    Salient Object Detection in Hyperspectral Images Using Felzenswalb’s Segmentation Algorithm
    (Springer Science and Business Media Deutschland GmbH, 2024) Lone, Z.A.; Pais, A.R.
    Salient 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.
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    Object detection in hyperspectral images
    (Elsevier Inc., 2022) Lone, Z.A.; Pais, A.R.
    Object Detection is a task of estimating and locating an object precisely in an image. It is a fundamental problem in computer vision and has been studied extensively in low dimensional images like RGB, grayscale, etc. High dimensional images like Hyperspectral images (HSI) contain ample information and are very powerful in enhancing the fine spectral differences between different objects. The advancement in spectral sensor technologies is making hyperspectral data more readily available, making it a promising technology for image analysis tasks. HSI has been explored in the fields of remote sensing, biomedical imaging, mineral classification, goods quality assessment, and object detection etc. The research concerning object detection in HSI has been gathering pace in recent times. This survey paper is an attempt to create a resource for researchers in the field. This paper provides a comprehensive review of both Supervised and Salient object detection. Moreover, a collection of important datasets is mentioned. We conclude the paper by mentioning research challenges and the future directions for the research in the field. © 2022 Elsevier Inc.