With the increasing popularity of OBIA, many scholars advocate that image segmentation plays a significant role in remote sensing image processing. Numerous segmentation algorithms for remote sensing images are based on region merging. Although good improvement is achieved, their accuracy is still dependent on parameter settings, leading to a low level of automation. To overcome this issue, this work proposes a new region merging method by using a random forest (RF) classifier. Unlike the traditional region merging methods that all adopt a scale threshold to determine whether a merging can be conducted, the new algorithm relies on a trained RF to decide the result of a merging test. Various merging criteria are simultaneously employed as feature variables of the RF model, enhancing the quality of the proposed scheme. The major problem in this work is how to train the RF classifier since the merging test samples need to be obtained in the iterative steps of a region merging process, which involves a huge number of human–computer interactions even for a small image. To simplify it, a sample collection strategy based on a set of three-scale segmentation results is devised. Representative merging test samples can be obtained by using this method. To validate the proposed technique, four Gaofen-2 images are used for training sample collection, and the most interesting result is that the samples extracted from one image can apply to others. Some images captured by Orbview-3, GeoEye-1, and Worldview-2 further indicate the robust performance of the new algorithm and the samples acquired in this work.

随着OBIA的日益普及，许多学者主张图像分割在遥感图像处理中起着重要作用。用于遥感图像的许多分割算法是基于区域合并的。尽管已经取得了很好的改进，但是它们的精度仍然取决于参数设置，导致自动化程度较低。为了克服这个问题，这项工作提出了一种使用随机森林（RF）分类器的新区域合并方法。与传统的区域合并方法都采用比例阈值来确定是否可以进行合并不同，新算法依赖于训练有素的RF来决定合并测试的结果。同时将各种合并标准用作RF模型的特征变量，从而提高了所提出方案的质量。这项工作的主要问题是如何训练RF分类器，因为需要在区域合并过程的迭代步骤中获得合并的测试样本，这甚至涉及到很小的图像也涉及大量的人机交互。为了简化它，设计了基于一组三尺度分割结果的样本收集策略。使用此方法可以获得代表性的合并测试样本。为了验证所提出的技术，使用了四个高分2图像来训练样本收集，最有趣的结果是从一个图像中提取的样本可以应用于其他图像。Orbview-3，GeoEye-1和Worldview-2捕获的一些图像进一步表明了该新算法的稳健性能以及在这项工作中获得的样本。