Hierarchical segmentation of range images inside the combinatorial pyramid

Marfil, Rebeca; Antúnez, E.; Bandera, Antonio

doi:10.1016/j.neucom.2015.01.075

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…On the contrary, if spatial relations are overemphasized, the OMRF model will lead to oversmooth results. Hierarchical processing provides an effective strategy for spatial relationship analysis (Marfil, Bandera, 2015). 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.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Based on Hierarchical Segmentation Tree With Auxiliary Labels

Zhang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%