Minimizing the negative effect of the overlapping pixels on the classification accuracy of the error back-propagation neural network classifier using the ancillary and supplemental data

摘要

A procedure is presented for minimizing the problems associated with the overlapping land cover spectral signatures on the classification accuracy, which involves the use of ancillary and supplemental data in remote sensing classification. Multispectral remote sensing data from a section of Kananaskis Country in the Rocky Mountains in Canada were used as an application for this investigation. A technique was established to simplify the method of selecting the dataset of the spectral training sample that can be used to determine decision rules for classifying the application area and improving the sorting process between the overlapping pixels. A three-stage computer classifier model was developed based on the back-propagation artificial neural network method to enhance the sorting process among land cover types that have similar spectral signatures. The performance of this computer model was assessed by comparing the results with the results of three prepackaged computer classifiers using the same dataset: maximum likelihood, minimum distance to means, and parallelepiped. The investigation demonstrated that the developed computer model performed satisfactorily overall with the use of the proposed enhanced sorting procedure.