Data resolution versus forestry classification and modelingThis paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be better than the accuracies for data with higher resolution. The increase in accuracy is constructed as due to the reduction of scene inhomogeneity at lower resolution. The computer classification scheme was a maximum likelihood classifier.
Document ID
19760035942
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Kan, E. P. (Lockheed Electronics Co. Houston, TX, United States)
Ball, D. L. (Lockheed Electronics Co. Houston, TX, United States)
Basu, J. P. (Lockheed Electronics Co., Inc. Houston, Tex., United States)
Smelser, R. L. (U.S. Department of Agriculture, Forest Service, Lufkin Tex., United States)
Date Acquired
August 8, 2013
Publication Date
January 1, 1975
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Symposium on Machine Processing of Remotely Sensed Data