Knowledge-based classification of Landsat Thematic Mapper digital imagery

Date of Completion

January 1987

Keywords

Environmental Sciences|Remote Sensing|Artificial Intelligence

Degree

Ph.D.

Abstract

An innovative approach to identifying land use and land cover categories through computer-assisted analyses of digital satellite remote sensing image data is presented. The technique is based on the subfield of artificial intelligence known as expert systems, commonly referred to as knowledge-based systems. The methodology developed embodies expert image analyst rules and heuristics in a knowledge base which is used to classify regions of a Landsat Thematic Mapper image. Expert knowledge about and image attributes from spectral, spatial, and temporal domains are addressed. The procedures for image and ancillary data preprocessing, knowledge acquisition, knowledge-based image analysis, and traditional image classification are discussed.^ Both the deterministic knowledge-based image analysis approach and the traditional statistical maximum likelihood approach were applied to multidate Landsat Thematic Mapper digital imagery to derive land use and land cover information. It was found that a knowledge-based approch to the classification of specially-processed, digital remote sensing imagery, coupled with spatial information, produced results superior, in terms of accuracy and visual comprehensibility, to those achieved through conventional per-pixel, supervised classification of multispectral data alone. It was illustrated that the knowledge-based method developed permits the inclusion of heuristics and decision criteria not possible in the strictly numerical, algorithmic approach. Because of the success of this prototype knowledge-based image analysis system, and because of its parallelism with human visual image analysis processes, additional research into this area is recommended and briefly discussed. ^

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