Jerrold E. Baum

TL;DR: In this article, an end-to-end remote sensing system modeling approach was developed to study system parameters in the context of land cover classification, 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.

TL;DR: This paper extends an end-to-end remote sensing system modeling approach 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 versus false alarm curves to characterize performance.

TL;DR: This work presents theory for the analytical transformation of surface statistics to at-sensor spectral radiance statistics for a downward-looking hyperspectral sensor observing both reflected sunlight and thermally emitted radiation.

TL;DR: A short study was undertaken which first reviewed the literature for discussions of the sensor's noise characteristics and then extended those results with additional analyses of HYDICE data, showing that, although a number of sensor artifacts exist, they have little impact on the interpretations of HSI utility derived from analyses of data.