A Hyper-Spectral Image (HSI) has high spectral and low spatial resolution. As a result, most targets exist as subpixels, which pose challenges in target detection. Moreover, limitation of target and background samples always hinders the target detection performance. In this thesis, a hybrid method for subpixel target detection of an HSI using minimal prior knowledge is developed. The Matched Filter (MF) and Adaptive Cosine Estimator (ACE) are two popular algorithms in HSI target detection. They have different advantages in differentiating target from background. In the proposed method, the scores of MF and ACE algorithms are used to construct a hybrid detection space. First, some high abundance target spectra are randomly picked from the scene to perform initial detection to determine the target and background subsets. Then, the reference target spectrum and background covariance matrix are improved iteratively, using the hybrid detection space. As the iterations continue, the reference target spectrum gets closer and closer to the central line that connects the centers of target and background and resulting in noticeable improvement in target detection. Two synthetic datasets and two real datasets are used in the experiments. The results are evaluated based on the mean detection rate, Receiver Operating Characteristic (ROC) curve and observation of the detection results. Compared to traditional MF and ACE algorithms with Reed-Xiaoli Detector (RXD) background covariance matrix estimation, the new method shows much better performance on all four datasets. This method can be applied in environmental monitoring, mineral detection, as well as oceanography and forestry reconnaissance to search for extremely small target distribution in a large scene. iv ACKNOWLEDGEMENT The first course I took at the University of Nevada, Las Vegas was "Coding with Applications in Computers and Communication Media" instructed by Dr. Shahram Latifi. This course triggered my enthusiasm in mathematics which had been concealed for many years. My master study at UNLV is memorable for me, for it is a valuable experience to learn some advanced knowledge in both engineering and math. I am grateful to many people who help me accomplish it. I would like to express my great gratitude to my advisor, Dr. Shahram Latifi. Without his help, my graduate study in the U.S. would not become true. He is a gracious professor and dedicated researcher. I would like to thank him for his encouragement, guidance and support during my master study. I would like to thank Dr. Yahia Baghzouz for his kind support and insightful instruction. I
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