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摘要： 多尺度分割是面向对象图像分析技术的前提和关键，多尺度分割的质量直接影响着面向对象分类的精度，但尺度选择仍然是多尺度分割中的一个难题。针对此问题，根据遥感影像的最优分割尺度与影像上目标复杂度密切相关的事实，提出了一种自上而下基于分割对象复杂度选取最优尺度的方法。该方法在分割过程中，提取每一对象的影像特征构建其复杂度函数，通过设置阈值，经迭代计算来确定每一对象的最优分割尺度，进而得到具有全局最优尺度的分割结果，并将其应用于ZY-3多光谱数据和GF-2融合影像，得到分割和分类结果。并将其与单一最优尺度和非监督评价法的分割及分类结果进行比较，结果表明：该方法能够获取与地面目标相匹配的分割尺度，改善了分割效果，提高了分类精度，具有一定实用价值。 关键词： Meanshift分割;  面向对象图像分析技术;  对象复杂度;  最优分割尺度;  尺度选取及评价     Abstract: Multi-scale segmentation is the premise and key step of Object-Based Image Analysis (OBIA). The quality of multi-scale segmentation directly affects the accuracy of object-oriented classification. However, scale selection and evaluation remains a challenge in multi-scale segmentation. According to the fact that the optimal segmentation scale of the remote sensing image is closely related to the complexity of the objects of the image, a top-down method to select the optimal scale based on the complexity of segmented objects is proposed. In the top-down segmentation process, image features of each segmented object are extracted to construct the complexity function, and the optimal scale of each object is determined by setting a threshold value and iterating calculation. Then, the segmentation results with the best scale are obtained and applied to the ZY-3 satellite multispectral image and the GF-2 fusion image to obtain segmentation and classification results. Qualitative visual evaluation method, unsupervised evaluation method and supervised classification evaluation method were used to compare them with results obtained by the optimal single-scale segmentation and the unsupervised evaluation method. The experimental results show that the method can accurately obtain the scale matching with the ground targets, and improve segmentation effect and the classification accuracy, it is of practical value.

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