{'fraction_non_alphanumeric': 0.019525801952580194, 'fraction_numerical': 0.0, 'mean_word_length': 5.552511415525114, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spectral imaging system analytical model for subpixel object detection

Data from multispectral and hyperspectral imaging systems have been used in many applications including land cover classification, surface characterization, material identification, and spatially unresolved object detection. While these optical spectral imaging systems have provided useful data, their design and utility could be further enhanced by better understanding the sensitivities and relative roles of various system attributes; in particular, when application data product accuracy is used as a metric. To study system parameters in the context of land cover classification, an end-to-end remote sensing system modeling approach was previously developed. In this paper, we extend this model to subpixel object detection applications by including a linear mixing model for an unresolved object in a background and using object detection algorithms and probability of detection (P/sub D/) versus false alarm (P/sub FA/) curves to characterize performance. Validations with results obtained from airborne hyperspectral data show good agreement between model predictions and the measured data. Examples are presented which show the utility of the modeling approach in understanding the relative importance of various system parameters and the sensitivity of P/sub D/ versus P/sub FA/ curves to changes in the system for a subpixel road detection scenario.