Semantic segmentation is one of the important techniques in remote sensing image analysis. Existing methods, such as methods based on deep convolutional neural networks, etc., have made significant progress in semantic segmentation, but these methods often require a large amount of training data. The Markov random field model(MRF) based on the graph model proposed an idea of unsupervised semantic segmentation that did not rely on training data, which could effectively describe the spatial relationship of objects, and analyze the spatial distribution of objects. However, the existing MRF model methods were usually based on a single granularity primitive based on pixels or objects, and it is difficult to make full use of image information, resulting in poor semantic segmentation. Aiming at the above problems, this paper introduced the alternative direction method of multipliers(ADMM) and discretized it, then proposed an unsupervised semantic segmentation method(MRF-ADMM) based on the pixel and object primitives collaborative MRF model. Firstly, it constructed two probability maps of pixel primitive and object primitive, in which the pixel primitive probability map was used to describe the detailed information of the image and maintain the boundary of semantic segmentation. And the object primitive probability map was used to describe a large range of spatial relationships and deal with the high heterogeneity inside the remote sensing images. In the process of model solving, according to the characteristics of pixels and object primitives, it proposed a discretized ADMM method, and used it to transfer and update of the two primitive category labels. Compared with the existing MRF models, the experimental results of semantic segmentation of different types of remote sensing images in different databases such as Gaofen-2 and aerial images can effectively synergize the advantages of different granularity primitives and optimize semantics results.

[2023-01-05] Accepted Paper /doc/2022.09.0524/v1

[2023-07-05] Printed Article /doc/2022.09.0524/v2