Deep Learning-Based Detection in Hyperspectral Images

Abstract

Hyperspectral imaging has emerged as a powerful technique for capturing detailed spectral information across numerous wavelength bands, enabling accurate analysis of materials and objects. However, the high dimensionality and complexity of hyperspectral data make traditional detection methods less effective and computationally expensive. This study presents a deep learning-based approach for detecting and analyzing patterns in hyperspectral images. The proposed method leverages advanced neural network architectures to automatically extract spectral-spatial features and improve detection accuracy. Data preprocessing techniques, including normalization and dimensionality reduction, are applied to enhance model performance. Experimental results demonstrate that the deep learning model outperforms conventional methods in terms of accuracy, efficiency, and robustness. The approach is particularly useful in applications such as environmental monitoring, agriculture, mineral exploration, and surveillance. This work highlights the potential of deep learning to address challenges in hyperspectral image detection and provides a foundation for future research in this domain