Processing remote sensing data to categorize lake bed surficial substrates

Abstract

This work is concerned with lake bed categorization in terms of surficial substrates using remote sensing data. A single beam echosounder coupled with a RoxAnn/sup TM/ bottom classification sensor were used. To improve the current design and to better interpret the output signal from RoxAnn, several bottom classifiers were developed by using statistical methods. Although the methods utilized belong to an off-line procedure, the classifiers obtained are applicable to online isolation and useful for automated classification of underlake terrain. The application considered is the finding of areas suitable for trout spawning on the Minnesota shoreline of Lake Superior.