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The uniqueness and complexity of geological settings create challenges for hyperspectral sensing applications in mineral exploration. The optimization of an airborne hyperspectral survey design is a necessary step providing constraints on spectral signatures of the target, sensors used, and best flight parameters. Herein, we present a versatile software tool called HYSIMU that can be used to simulate different flight/sensor scenarios using mineral reflectance spectra from the USGS spectral library and find the most optimum survey design for a specific surface target. Random or selectable mineral reflectance spectra were assigned to synthetic random fields to create mineral distributions on synthetic digital elevation models with added spatial noise, spectral noise, and sun shadow effect. Several flights were simulated with various flight parameters such as altitude (10-200 meters), speed (1-100 m/s), and sun elevation (0, 45, 90 degrees). Several target scenarios with different levels of complexity and a 224-band hyperspectral sensor were simulated. The synthetic hyperspectral data generated from each flight and scenario were classified using ENVI. A sensitivity study was done by comparing the classification maps obtained and the ground truth using four different methods: Hausdorff Distance, Structural Similarity Index, Equal Pixel Percentage, and Correlation Coefficient. The results show a dependency of the classification maps on altitude (best for 10-50 meters) and slow flight speeds (best for 1-10 m/s) while the sun elevations did not cause any observed change in this altitude range. The results show that this toolkit can simulate any type of mineral exploration target from various airborne platforms and different flight parameters towards optimization of hyperspectral surveys.
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