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Proceedings Paper

Automatic recognition of USGS land use/cover categories using statistical and neural network classifiers
Author(s): Robert R. Bailey; Elaine J. Pettit; Ronald T. Borochoff; Michael T. Manry; Xianping Jiang
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Paper Abstract

This report presents a description of part of an end-to-end system being developed for the automatic recognition of general land use/cover categories from digitized aerial photography. Standard USGS categories are used here and include urban, fields, trees, and water. The system consists of modules for segmentation, feature extraction, and classification. This report extends the results of our efforts on the feature extraction and classification portions of the system, which were partially described earlier. Since the data source is panchromatic, the features used are measures of texture. These include spatial gray-level co-occurrence matrix, Laws, Fourier domain rings and wedges, and a simultaneous autoregressive model. The classifiers employed include the Bayes quadratic, k-nearest neighbor, Parzen, and a multilayer perceptron neural network. Through leave-one-scene-out sampling, each classifier type is trained and tested using feature data generated by each feature extraction technique. A new, fast method of training the multilayer perceptron is described. It is expected that many of the techniques developed will be applicable to other areas of image recognition where texture is an important discriminant.

Paper Details

Date Published: 24 September 1993
PDF: 11 pages
Proc. SPIE 1944, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision, (24 September 1993); doi: 10.1117/12.155803
Show Author Affiliations
Robert R. Bailey, E-Systems (United States)
Elaine J. Pettit, E-Systems (United States)
Ronald T. Borochoff, E-Systems (United States)
Michael T. Manry, Univ. of Texas/Arlington (United States)
Xianping Jiang, Univ. of Texas/Arlington (United States)

Published in SPIE Proceedings Vol. 1944:
Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision
Eamon B. Barrett; David M. McKeown Jr., Editor(s)

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