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Many factors influence classification accuracy and a typical error budget includes uncertainty arising from the 1) selection of processing algorithms, 2) selection of training sites, 3) quality of orthorectification, and 4) atmospheric effects. With the development of high spatial resolution imagery, the impact of errors in geographic coregistration between imagery and field sites has become apparent – and potentially limiting – for classification applications, especially those involving patchy target detection. The goal of this study was to document and quantify the effect of coregistration error between imagery and field sites on classification accuracy. Artificial patchy targets were randomly placed over a study area covered by a QuickBird image. Classification accuracy of these targets was assessed at two levels of coregistration. Results showed that producer's accuracy of target classification increased from 37.5% to 100% between low and high levels of coregistration respectively. In addition, “Error due to Location”, a measure of how well pixels were located within respective classes, decreased to zero at high coregistration levels. This study highlights the importance of considering coregistration between imagery and field sites in the error budget, especially with studies involving high spatial resolution imagery and patchy target detection. |
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