Target Detection by Optimizing Anomaly Detection in Hyperspectral Image Processing using AI/ML
DOI:
https://doi.org/10.32628/CSEIT25112816Keywords:
Hyperspectral Imaging (HSI), Anomaly Detection, Target Detection, Machine Learning (ML) Artificial Intelligence (AI), Deep Learning, Dimensionality Reduction, Principal Component Analysis (PCA), Patch-based Analysis, Spectral-Spatial Features, Image Preprocessing, Neural Networks, TensorFlow/Kera’s, Data Normalization, Feature Extraction, Supervised Learning, Unsupervised Learning, Classification, Object Detection, Remote SensingAbstract
Anomaly detection in hyperspectral images involves identifying deviations or outliers within the high-dimensional spectral data captured across numerous contiguous wavelength bands. Hyperspectral imaging provides detailed spectral information, making it a powerful tool for detecting subtle variations in materials or objects that are not visible in traditional imaging techniques. The proposed system employs advanced machine learning techniques, including convolutional neural networks (CNNs) and autoencoders, to analyse hyperspectral images for anomalies. By training the models on a dataset of normal hyperspectral images, the system learns the inherent spectral characteristics and identifies patterns of typical data. New hyperspectral data is then analysed to detect deviations that may indicate potential anomalies. This approach is particularly effective in applications such as remote sensing, environmental monitoring, precision agriculture, mineral exploration, and quality control, where detecting anomalies like land degradation, crop stress, or material defects is crucial.
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