409 
FACTOR ANALYTIC TECHNIQUES FOR ENHANCEMENT 
OF TEMPORAL INFORMATION IN DIGITAL IMAGERY 
Fredrick C. Luce 
Senior Research Technologist 
Brian J. Turner 
Associate Professor of Forest Management 
Co-Director, Office for Remote Sensing of Earth Resources 
Office for Remote Sensing of Earth Resources 
Institute for Research on Land and Water Resources 
The Pennsylvania State University 
University Park, PA 16802 
ABSTRACT 
Under contract with the Defense Mapping Agency, four digital processing 
techniques for detecting change in digitized aerial photos using the 
ORSER software system were evaluated. These four techniques are as 
follows: 1) digital post-classification comparison of classified scenes 
from two dates; 2) classifying the two date-image data as one data set; 
3) density-slicing ratios and differences between the two data sets; 
and 4) finding a transformation to highlight temporal information using 
factor analysis. All four techniques were applied to an initial test 
site and the factor analytic technique was shown to be the most success 
ful for mapping temporal information. This technique was then applied 
to four additional test sites and further evaluated. Initially, five 
factor rotations were used on each test site, using all the principal 
components. The best transformation was selected, density sliced, and 
displayed to highlight the temporal information. A similar analysis 
was then applied to the first few principal components. This, however, 
did not improve the enhancement of the temporal information. 
1. INTRODUCTION 
An efficient method of analyzing land-use change over time involves the 
use of remote sensing techniques. In the past, detection of change was 
accomplished by visual interpretation of photographs. The photointer- 
preted classification results from an area at some initial time were 
compared (manually and visually) to the classification of the area at a 
later date (Theis, 1979). Another visual method was the "blink" pro 
cess. This involved viewing the two images in a rapid or blinking 
fashion (Masry et al., 1975). This process is both tiring for the 
operator and insensitive to subtle image differences. With large areas 
of multiple-date imagery, manual analysis of temporal information has 
been a time-consuming and labor-intensive task. Although these methods 
have been useful and accurate, they have not taken advantage of comput 
erized techniques for data handling and storage. 
While digital analysis of imagery has allowed more efficient analysis of 
temporal information, particularly over large areas and several dates of 
data collection, the techniques used in the analysis have essentially 
remained the same. That is, a digital classification from one date has 
been compared (visually, manually, and/or digitally) to a digital classi 
fication of the same area at a second date. The numerical nature of 
digital data, however, lends itself to new methods for highlighting 
temporal information. These methods include image differencing and