本研究以粗糙集方法進行水稻田之辨識作業，採用嘉義地區多時段Formosat-2 影像及新竹地區多時段SPOT 影像為資料來源。實驗中，將粗糙集方法與高斯最大似然分類法及倒傳遞類神經網路進行分類成果比較。由實驗成果顯示，粗糙集方法於嘉義實驗區其整體精度為86.947%、Kappa 值為0.73826；於新竹實驗區其整體精度為81.44%、Kappa 值為0.61448。兩實驗區中，粗糙集方法的分類精度皆優於高斯最大似然分類法，但較倒傳遞類神經網路為差。

This study investigates the application of the Rough Set Theory for image classification. The images used for the experiment include multi-temporal Formosat-2 images of the Chiayi area and multi-temporal SPOT images of the Hsinchu area. Gaussian Maximum Likelihood Classification and Back-Propagation neural network are used for comparison. The overall accuracy for Rough Set Theory is 86.947% for Chiayi and 81.44% for Hsinchu. The kappa index is 0.73826 for Chiayi and 0.61448 for Hsinchu. In terms of the classification accuracy, Rough Set Theory is shown to be better than Gaussian Maximum Likelihood Classification but inferior to Back-Propagation neural network for Chiayi and Hsinchu area.