Abstract
Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.
Highlights
Semantic segmentation is defined as a multi-label classification problem (Hu et al, 2018), which aims to assign category labels to each pixel on the image
The classical method of semantic segmentation based on the MRF model is Pixel-based Markov Random Field (PMRF) (Geman, S, 1984, Besag, 1993)
With the application of multi-scale strategy and object-based method in remote sensing imagery segmentation, the Objectbased Markov Random Field (OMRF) and the MRF model based on hierarchical information is widely used in the process of remote sensing imagery semantic segmentation
Summary
Semantic segmentation is defined as a multi-label classification problem (Hu et al, 2018), which aims to assign category labels to each pixel on the image. The MRF model makes full use of spatial information constraints, which is suitable for spectral and texture processing of remote sensing imagery. The classical method of semantic segmentation based on the MRF model is Pixel-based Markov Random Field (PMRF) (Geman, S, 1984, Besag, 1993). With the application of multi-scale strategy and object-based method in remote sensing imagery segmentation, the OMRF and the MRF model based on hierarchical information is widely used in the process of remote sensing imagery semantic segmentation. Inspired by the interaction between layers based on the conditional probability distribution model (Zheng et al, 2017), this paper establishes the Object-based Markov Random Field based on hierarchical segmentation tree with auxiliary labels (OMRF-HA) to realize the semantic segmentation.
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