An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

Abstract

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.

Keywords:

• spatial decision support system
• remote sensing
• BPN+EDBD algorithm
• image classification

Acknowledgement

The authors express their gratitude for the research assistants of GIS Research Center, Feng Chia University, for providing all the relevant paddy rice data on this study and National Science Council (97-2625-M-275-001-) and (NSC-97-2116-M-035-001) also sponsored this work.

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