Chemical Agent Resistant Coating (CARC) is the term for the paint commonly applied to military vehicles which provides protection against chemical and biological weapons. In this paper, we present results for detecting CARC with two different colors (Green and Beige). A High-Fidelity Target Insertion Method has been developed. This method allows one to insert the target radiance into any HSI sensor scene, while still to preserve the sensor spatio-spectral noise at all the pixel positions. We show that the reduced (400-1,000nm) spectral range is good enough for Beige CARC, and Green CARC Type I and II detection using several current state-of-the-art HSI target detection methods. Furthermore, we will present a newly developed Feature Transformation (FT) algorithm. In essence, the FT method, by transforming the original features to a different feature domain (e.g., the Fourier, wavelet packets, and local cosine domains), may considerably increase the statistic separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral features (this operation can be considered as the 1st level Haar wavelet high-pass filtering), we can completely separate Beige CARC from the background using a single band at 650nm, and completely separate Green CARC from the background using a single band at 1180nm, leading to perfect detection results. We have developed an automated best spectral band selection process that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also developed an automated crossspectrum fusion process to further improve the detection performance in lower spectral range (<1,000nm) by selecting the best spectral band pair.