Paper
16 October 1998 Sensor design considerations for HSI remote sensing
Robert A. Keller, Sylvia S. Shen, Alfred T. Pritt Jr.
Author Affiliations +
Abstract
Hyperspectral imaging (HSI) technique in remote sensing gauges its performance based on the usefulness of the products. Many of the products consists of the detection and identification of various materials within an imaging element or pixel. Nearly all HSI systems image at a level in which several materials may compromise a single pixel. We have derived an expression for the sub-pixel detection capabilities assuming complete knowledge of background and target spectra and the spectral influence of the atmosphere. This derivation assumes that a pixel is comprised of a set of background and target spectral components which when added together, weighted by the area extent, produce the observed spectrum of the pixel. The analysis is done for two sensor types: one that is photon-noise limited and one that is detector-noise limited. Results of this analysis show that the basic sensor parameters can be separated effectively from phenomenology limitations, thus providing an ability to trade sensor parameters, leading to an understanding of their effect on target detection. This derivation forms a framework for discussing conditions for which backgrounds and the atmospheric interferences are not completely known and for cases in which the target spectrum may or may not be known. These results are compared to analyses directed at empirically understanding sensor trades.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert A. Keller, Sylvia S. Shen, and Alfred T. Pritt Jr. "Sensor design considerations for HSI remote sensing", Proc. SPIE 3438, Imaging Spectrometry IV, (16 October 1998); https://doi.org/10.1117/12.328091
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Target detection

Sensors

Signal to noise ratio

Reflectivity

Spectral resolution

Optical filters

Remote sensing

Back to Top