This website is best viewed in a browser that supports web standards.
Aerial imagery from the 1930s to the early 1990s was predominantly acquired using black and white film. Its use in remote sensing applications and GIS analysis is constrained by its limited spectral information and high spatial resolution. As a historical record and to study long-term land use/land cover change this imagery is a valuable but often underutilized resource. Traditional classification of gray level aerial photos has primarily relied on visual interpretation and digitizing to obtain land cover classifications that can be used in a GIS. This is a time consuming and labor intensive process that can often limit the scale of analysis.
This research focused on the use of digital image processing to facilitate visual interpretation and heads up digitizing of gray level imagery. Existing remote sensing software packages have limited functionalities with respect to classifying black and white aerial photos. Traditional image classification alone provides limited results when determining land cover types derived from gray level imagery. This research examined approaching classification as a system which uses digital image processing techniques such as filtering, texture analysis and principle components analysis to improve supervised and unsupervised classification algorithms to provide a base for digitizing land cover types in a GIS. Post processing operations included smoothing the classification result and converting it to a vector layer that can be further refined in a GIS. Software tools were developed using ArcObjects to aid the process of refining the vector classification. These tools improve the usability and accuracy of the digital image processing results that help facilitate the visual interpretation and digitizing process to gain a usable land use/land cover classification from gray level imagery.