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@article{188077,
        author = {Tapan Belapurkar and Aayush Girish Kalhapure and C E Dhakshesh and Akshad Bharate},
        title = {Anomaly Detection in Hyperspectral Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {568-574},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188077},
        abstract = {Hyperspectral imaging (HSI) captures information in the very narrow spectral bands over a broad spectrum, so it is employed for recognizing substances and discriminating between aberrations in fields such as remote sensing, agriculture, and military security. This research looks at several methods for spotting anomalies using hyperspectral image data from the Pavia University dataset. The methods include the RX (Reed-Xiaoli) detector, Support Vector Data Description (SVDD), an autoencoder model made in PyTorch, and a fusion model. A fusion strategy combined the strengths of these different models. We improved dataset relevance and quality through preprocessing. We evaluated each method using confusion matrices and compared them to other approaches. The analysis shows that mixing traditional statistical techniques with deep learning methods greatly enhances anomaly detection performance in hyperspectral images.},
        keywords = {Hyperspectral Imaging, Anomaly detection, Autoencoder, SVDD, RX detector, fusion model.},
        month = {December},
        }