This thesis proposes a method for comparing real and simulated hyperspectral imagery by examining the characteristics of simulated imagery in comparison to real imagery acquired with multiple sensors hosted on an airborne platform. The dataset includes aerial multi- and hyperspectral imagery with spatial resolutions of one meter or less. The multispectral imagery includes data from an airborne sensor with three-band visible color and calibrated radiance imagery in the long-, mid-, and short-wave infrared. The airborne hyperspectral imagery includes 360 bands of calibrated radiance and reflectance data spanning 400 to 2450 nm in wavelength. Collected in September 2012, the imagery is of a park in Avon, NY, and includes a dirt track and areas of grass, gravel, forest, and agricultural fields. A number of artificial targets were deployed in the scene prior to collection for purposes of target detection, subpixel detection, spectral unmixing, and 3D object recognition. A synthetic reconstruction of the collection site was created in DIRSIG, an image generation and modeling tool developed by the Rochester Institute of Technology, based on ground-measured reflectance data, ground photography, and previous airborne imagery. Simulated airborne images were generated using the scene model, time of observation, atmospheric conditions, and knowledge of the sensor characteristics. The thesis provides a comparison between the empirical and simulated images, including a comparison of achieved performance for classification, detection and unmixing applications. It was found that several differences exist due to the way the image is generated, including finite sampling and incomplete knowledge of atmospheric conditions and sensor characteristics. The lessons learned from these differences can be used to refine the modeling tool and its use as part of ongoing efforts to increase the realism of the simulated data.

Library of Congress Subject Headings

Multispectral imaging; Remote-sensing images; Classification

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


John P. Kerekes

Advisor/Committee Member

Jeff B. Pelz

Advisor/Committee Member

Jan A. van Aardt


Physical copy available from RIT's Wallace Library at G70.4 .B56 2014


RIT – Main Campus

Plan Codes