Fusion of hierarchical class graphs for remote sensing semantic segmentation

计算机科学 分割 班级(哲学) 模式识别(心理学) 不相交集 尺度空间分割 图形 图像分割 人工智能 像素 基于分割的对象分类 关系(数据库) 分层数据库模型 数据挖掘 数学 理论计算机科学 组合数学
作者
Xudong Kang,Yintao Hong,Puhong Duan,Shutao Li
出处
期刊:Information Fusion [Elsevier]
卷期号:109: 102409-102409 被引量:2
标识
DOI:10.1016/j.inffus.2024.102409
摘要

Semantic segmentation of remote sensing images aims to assign a specific label or class to each pixel in an image, which plays an extremely important role in scene understanding. Currently, many advanced deep learning-based semantic segmentation methods have been developed. However, these methods are always based on disjoint labels to identify ground objects while ignoring the correlation (e.g., semantic, shapes, materials, etc.) among different ground objects, which limits the segmentation performance of remote sensing images. To solve this issue, we propose a hierarchical class graph for semantic segmentation of high resolution remote sensing images, which can learn structured relation among different ground objects. Specifically, first, we construct hierarchical class graphs based on different attributes and layers. Then, a three-layer hierarchical segmentation framework is developed to learn the correlation among different ground objects. Finally, a decision fusion method is designed to fuse the benefits of different hierarchical attributes and layers. More importantly, the influence of different hierarchical class graphs on the segmentation performance is detailedly analyzed. Extensive experiments on the iSAID and Vaihingen datasets reveal that all studied segmentation methods with hierarchical class graph can obtain better segmentation performance compared to ones without hierarchical class graph. The limitation of the proposed method is that the training time of the segmentation model tends to increase a bit because of considering the correlation among different ground objects.
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