Convolutional neural network (CNN) has been widely used in semantic segmentation for
remote sensing images, and it has achieved great success. Due to the diversity of the spatial …

With the continuous development of edge computing, real-time processing of satellite
images has gradually become a research hotspot. However, the current semantic …

The Markov random field (MRF) model has attracted great attention in the field of image
segmentation. However, most MRF-based methods fail to resolve segmentation …

This paper proposes a novel object-based Markov random field model (OMRF) for semantic
segmentation of remote sensing images. First, the method employs the region size and edge …

Semantic segmentation is one of the most important tasks in remote sensing. In the semantic
segmentation of remote sensing images, some regions are repeatedly transformed between …

Semantic segmentation is one of the most important tasks in remote sensing. However, as
spatial resolution increases, distinguishing the homogeneity of each land class and the …

Markov random field model (MRF) has attracted great attention in the field of image
segmentation. Its basic unit can be pixels or regions. These pixel‐based or region‐based …

Semantic segmentation is one of the most important tasks in the field of remote sensing
image processing. Many methods have been proposed to realize it at the pixel granularity or …

The Markov random field (MRF) model is a widely used method for remote-sensing image
segmentation, especially the object-based MRF (OMRF) method has attracted great …

High-spatial-resolution (HSR) remote sensing images usually contain rich hierarchical
semantic information. However, many methods fail to solve the segmentation …