To meet the large demand of hyperspectral images for hyperspectral anomaly detection, a hyperspectral image generation method was proposed based on the hyperspectral characteristic simulation data and background characteristic data of the target. The flow and heat transfer model, infrared radiation characteristic model, and hyperspectral image simulation model were studied. The aircraft reflectivity measured by experiment was taken as the input for the calculation of target characteristics. Combined with the actual observed background undulating image, hyperspectral images with different pixel abundances and signal-to-noise ratios were generated under the conditions such as specific spectral response characteristics of remote sensor, relative calibration error of remote sensor, and random additive noise. The abnormal pixels of the simulation image were detected by RX algorithm and CEM algorithm. Results show that the model could generate hyperspectral subpixel simulation images based on the performance parameters and target abundance requirements of the remote sensor. The image could reflect the impact of target aircraft pixel abundance and signal-to-noise ratio on the detection results. By adjusting the input parameters, the hyperspectral simulation image for subpixel anomaly detection could be efficiently constructed. When the RX algorithm was used to detect hyperspectral simulation image, the detection results were largely affected by the noise. When the signal-to-noise ratio was as low as 10 dB, it was difficult to detect the abnormal pixels by RX algorithm with the abundance less than 0.4. While the CEM algorithm based on spectral matching detection could detect anomalies and improve the detection probability under low pixel abundances and signal-to-noise ratios.