Object-based image analysis (OBIA) has been widely used for classifying high-spatial-resolution images, and the selection of scale parameter(s) is inevitable in previous OBIA tasks. However, selecting appropriate scale(s) for an application inherently depends on the objective of the application, which cannot be robustly solved at the segmentation stage. In this study, a novel framework without any scale parameter was proposed for object-based image classification. It consists of four major steps: (1) Scale-sets image representation: Multiscale segments from a region-merging algorithm are organized using a scale-sets structure, where each segment is represented as a node of the hierarchy. Note that the implementation of this step does not need any scale parameter. (2) Multiscale sample selection and enriching: Multiscale segmentation results are retrieved from the scale-sets structure and visualized, and then training samples are selected from multiple scales. The training samples are further enriched according to hierarchical relations. (3) Feature extraction and multiscale classification: Segments are described using spectral, textural and geometric features, and then classified using a Random Forest classifier. Through this step, all segments are labeled with a class label, and a classified scale-sets structure is obtained. (4) Automatic optimal classification map selection: A multiscale accuracy assessment is conducted to evaluate the performances at different scales, and the optimal classification map is selected. A QuickBird image and a Gaofen-2 image were used to demonstrate the effectiveness and advantages of the proposed approach. The experimental results demonstrated that the optimal scale of an object-based image classification work is a range, not a single value. Moreover, the multiscale accuracies obtained using training samples and ground truth maps showed the same tendency. Therefore, it is possible to automatically estimate the optimal classification scale using training samples. Besides, we have demonstrated the effectiveness of using hierarchical enriching strategy to improve the performance of object-image classification.

基于对象的图像分析 (OBIA) 已被广泛用于对高空间分辨率图像进行分类，并且在以前的 OBIA 任务中，尺度参数的选择是不可避免的。然而，为应用程序选择合适的尺度本质上取决于应用程序的目标，这在分割阶段无法稳健解决。在这项研究中，提出了一种没有任何尺度参数的新框架用于基于对象的图像分类。它包括四个主要步骤： (1) 尺度集图像表示：来自区域合并算法的多尺度段使用尺度集结构进行组织，其中每个段表示为层次结构的一个节点。请注意，此步骤的实现不需要任何比例参数。(2) 多尺度样本选择和富集：从尺度集结构中检索多尺度分割结果并进行可视化，然后从多个尺度中选择训练样本。根据层次关系进一步丰富训练样本。(3) 特征提取和多尺度分类：使用光谱、纹理和几何特征描述段，然后使用随机森林分类器进行分类。通过这一步，所有的段都被标记了一个类标签，得到了一个分类的尺度集结构。(4)自动最优分类图选择：进行多尺度精度评估，评估不同尺度下的性能，选择最优分类图。使用 QuickBird 图像和 Gaofen-2 图像来证明所提出方法的有效性和优势。实验结果表明，基于对象的图像分类工作的最佳尺度是一个范围，而不是单个值。此外，使用训练样本和地面实况图获得的多尺度精度显示出相同的趋势。因此，可以使用训练样本自动估计最佳分类尺度。此外，我们已经证明了使用分层丰富策略来提高对象图像分类性能的有效性。