This article provides an overview of the theoretical and practical aspects of hyperspectral image processing for automatic target detection. It focuses on techniques that use spectral information to classify pixels as targets or non-targets. The authors describe the fundamental structure of hyperspectral data, the influence of atmospheric conditions on signal models, and the approach to deriving detection algorithms. They discuss performance metrics and present a taxonomy of various algorithms. The article also includes empirical results using data from the HYDICE and Hyperion sensors to illustrate the operation and performance of different detectors. The discussion covers the challenges of spectral variability and mixed-pixel interference, and introduces models such as probability density, subspace, and linear spectral mixing models to characterize these issues. The article concludes with a detailed explanation of detector design, evaluation, and a roadmap for developing effective target detection algorithms.This article provides an overview of the theoretical and practical aspects of hyperspectral image processing for automatic target detection. It focuses on techniques that use spectral information to classify pixels as targets or non-targets. The authors describe the fundamental structure of hyperspectral data, the influence of atmospheric conditions on signal models, and the approach to deriving detection algorithms. They discuss performance metrics and present a taxonomy of various algorithms. The article also includes empirical results using data from the HYDICE and Hyperion sensors to illustrate the operation and performance of different detectors. The discussion covers the challenges of spectral variability and mixed-pixel interference, and introduces models such as probability density, subspace, and linear spectral mixing models to characterize these issues. The article concludes with a detailed explanation of detector design, evaluation, and a roadmap for developing effective target detection algorithms.