The classification tree (CT) may be used to establish explicit classification rules for Satellite Imagery (SI). However, the accuracy of explicit classification rules attained by this method is poor. Back-propagation networks (BPN) and the support vector machine (SVM) may both be used to establish highly accurate models for predicting the classification of SI. However, neither is able to generate explicit rules. This study proposes the evolutionary classification tree (ECT) as a novel mining rule method. Composed of the particle bee algorithm (PBA) and classification tree (CT), the ECT produces self-organized rules automatically to predict the classification of SI. In ECT, CT serves as the architecture to represent explicit rules and PBA acts as the optimization mechanism to optimize CT in order to fit the experimental data. A total of 600 experimental datasets were used to compare the accuracy and complexity of four model-building techniques: CT, BPN, SVM, and ECT. The results demonstrate the ability of ECT to produce rules that are more accurate than CT and SVM but less accurate than BPN. However, because BPN is black box model, the ability of ECT to generate explicit rules makes ECT the best model for users wanting to mine the explicit rules and knowledge in practical applications.

分类树（CT）可用于为卫星图像（SI）建立明确的分类规则。但是，这种方法获得的显式分类规则的准确性很差。反向传播网络（BPN）和支持向量机（SVM）均可用于建立高度准确的模型，以预测SI的分类。但是，它们都不能生成明确的规则。这项研究提出了进化分类树（ECT）作为一种新颖的挖掘规则方法。ECT由粒子蜂算法（PBA）和分类树（CT）组成，可自动生成自组织规则以预测SI的分类。在ECT中，CT充当表示显式规则的体系结构，而PBA充当优化机制以优化CT以适合实验数据。总共使用了600个实验数据集来比较四种模型构建技术（CT，BPN，SVM和ECT）的准确性和复杂性。结果证明了ECT产生规则的能力比CT和SVM更准确，但不如BPN准确。但是，由于BPN是黑盒模型，因此ECT生成显式规则的能力使ECT成为希望在实际应用中挖掘显式规则和知识的用户的最佳模型。