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Textural-Contextual Labeling and Metadata Generation for Remote Sensing ApplicationsDespite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.
Document ID
19990041150
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Kiang, Richard K. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1999
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: Aerosense
Location: Orlando, FL
Country: United States
Start Date: April 5, 1999
End Date: April 9, 1999
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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