Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{186762,
        author = {Aditya Singh and Nilesh N Thorat and Nilesh Kulal},
        title = {Target Detection by Optimizing Anomaly Detection in Hyperspectral Image Processing using AI/ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3177-3185},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186762},
        abstract = {Hyperspectral imaging (HSI) captures images across hundreds of contiguous spectral bands, enabling detailed material characterization and precise target detection. However, detecting small or hidden targets in complex hyperspectral scenes remains challenging due to high dimensionality, spectral variability, and background clutter. This paper presents an optimized anomaly detection framework leveraging artificial intelligence (AI) and machine learning (ML) algorithms to enhance target identifica- tion accuracy in hyperspectral image processing. The proposed model integrates dimensionality reduction, deep learning-based feature extraction, and adaptive threshold optimization. Exper- imental results demonstrate improved detection performance, reduced false alarm rates, and enhanced computational efficiency compared to traditional algorithms.},
        keywords = {Hyperspectral Imaging, Target Detection, Anomaly Detection, Machine Learning, AI Optimization, Spec- tral Signature, Deep Learning.},
        month = {November},
        }