Crop classification based on object-based image analysis (OBIA) is increasingly reported. However, it is still challenging to produce high-quality crop type maps by using recent techniques. This article introduces a new object-based crop classification algorithm which contains 4 steps. First, a random forest (RF) classifier is trained by using the initial training set, which tends to have a relatively small size. Second, importance scores for each feature variable are derived by using the RF model. Third, by treating the importance scores as weighting factors, a weighted Euclidean distance criterion is designed and used for sample creation to enlarge training set. Fourth, RF is re-trained by using the enlarged training set, and then it is employed for final classification. To validate the proposed strategy, a Worldview-2 image covering a part of Hetao plain is experimented. Results indicate that the new method yields the best overall accuracy, which equals 90.52%.

基于基于对象的图像分析（OBIA）的农作物分类的报道越来越多。然而，通过使用最新技术来产生高质量的作物类型图仍然是挑战。本文介绍了一种新的基于对象的农作物分类算法，该算法包含4个步骤。首先，通过使用初始训练集来训练随机森林（RF）分类器，该训练集倾向于具有相对较小的大小。其次，使用RF模型得出每个特征变量的重要性得分。第三，通过将重要性分数作为加权因子，设计了加权的欧几里得距离准则，并将其用于样本创建以扩大训练集。第四，通过使用扩大的训练集对RF进行训练，然后将其用于最终分类。为了验证提议的策略，实验了一张涵盖河套平原一部分的Worldview-2图像。结果表明，该新方法产生了最佳的总体准确度，等于90.52％。