Image segmentation is essential in object-based image analysis. Numerous image segmentation algorithms have been proposed and widely applied to process remote sensing images, but most of them are designed to deal with single scenes. As the volume of images grows rapidly, handling images with single machines is becoming increasingly difficult, and the size of a composite image can be larger than the CPU memory of a single computer. To address this problem, a distributed image segmentation strategy is proposed in this paper. The two main steps of the proposed strategy are as follows. First, a prepared massive image is loaded and then decomposed into sub-images that are distributed across multiple computers; algorithms are then used in parallel to segment each sub-image into a large number of initial objects. Secondly, the proposed object resegmentation method is applied to the initial boundary objects in each sub-image in order to merge these objects. The sub-images are then ingested from the different computers in order to obtain the final segmentation image. Two classical segmentation algorithms are employed to test the proposed strategy in eight different study areas that include urban area, suburban zone and agricultural landscape. Both the intersection over union and the F-measure metrics show that the proposed strategy can help to solve the problem of the data volume being too large to fit on a single machine, and that it also performs better than comparative strategies. The proposed strategy not only has the ability to segment very large images, but also accelerates the segmentation and segmentation-based applications so that they can match the image acquisition rate.

图像分割在基于对象的图像分析中至关重要。已经提出了许多图像分割算法并将其广泛地用于处理遥感图像，但是其中大多数被设计为处理单个场景。随着图像量的快速增长，使用单台计算机处理图像变得越来越困难，并且合成图像的大小可能大于单台计算机的CPU内存。为了解决这个问题，本文提出了一种分布式图像分割策略。拟议策略的两个主要步骤如下。首先，加载准备好的海量图像，然后分解为分布在多台计算机上的子图像。然后并行使用算法将每个子图像分割为大量初始对象。其次，提出的对象分割方法应用于每个子图像中的初始边界对象，以合并这些对象。然后从不同的计算机中摄取子图像以获得最终的分割图像。两种经典的分割算法用于在八个不同的研究领域（包括市区，郊区和农业景观）中测试所提出的策略。联合交集和F度量指标均表明，所提出的策略可以帮助解决数据量太大而无法容纳在一台机器上的问题，并且其性能也优于比较策略。提出的策略不仅可以分割非常大的图像，