A PERCEPTRON-BASED FEATURE SELECTION APPROACH FOR DECISION TREE CLASSIFICATION

Abstract: The use of OBIA for high spatial resolution image classification can be divided in two main steps, the first being segmentation and the second regarding the labeling of the objects in accordance with a particular set of features and a classifier. Decision trees are often used to represent human knowledge in the latter. The issue falls in how to select a smaller amount of features from a feature space with spatial, spectral and textural variables to describe the classes of interest, which engenders the matter of choosing the best or more convenient feature selection (FS) method. In this work, an approach for FS within a decision tree was introduced using a single perceptron and the Backpropagation algorithm. Three alternatives were compared: single, double and multiple inputs, using a sequential backward search (SBS). Test regions were used to evaluate the efficiency of the proposed methods. Results showed that it is possible to use a single perceptron in each node, with an overall accuracy (OA) between 77.6% and 77.9%. Only SBS reached an OA larger than 88%. Thus, the quality of the proposed solution depends on the number of input features.摘要：面向对象影像分析（Object-Based Image Analysis, OBIA）应用于高空间分辨率影像分类时，可分为两个核心步骤：其一为影像分割（segmentation），其二则是依据特定特征集与分类器对影像对象进行标注。在该环节中，决策树常被用于表征人类先验知识。当前的关键挑战在于，如何从包含空间、光谱与纹理变量的特征空间中筛选出少量特征以精准描述目标地物类别，由此催生了最优或更适配的特征选择（Feature Selection, FS）方法的选择议题。本研究提出了一种在决策树框架内开展特征选择的方案，该方案采用单层感知器与反向传播（Backpropagation）算法。研究对比了三种输入模式：单输入、双输入与多输入，并引入了序列后向搜索（Sequential Backward Search, SBS）方法。通过测试区域对所提方法的有效性进行验证与评估，结果表明，可在决策树的每个节点使用单层感知器，其总体精度（Overall Accuracy, OA）介于77.6%至77.9%之间；仅采用序列后向搜索的方案，其总体精度超过了88%。由此可见，所提方案的性能表现取决于输入特征的数量。