基于图卷积网络的多标签遥感图像分类

由于遥感图像包含物体类别多样,单个语义类别标签无法全面地描述图像内容,而多标签图像分类任务更加具有挑战性.通过探索深度图卷积网络(GCN),解决了多标签遥感图像分类缺乏对标签语义信息相关性利用的问题,提出了一种新的基于图卷积的多标签遥感图像分类网络,它包含图像特征学习模块、基于图卷积网络的分类器学习模块和图像特征差异化模块三个部分.在公开多标签遥感数据集Planet和UCM上与相关模型进行对比,在多标签遥感图像分类任务上可以得到了较好的分类结果.该方法使用图卷积等模块将多标签图像分类方法应用到遥感领域,提高了模型分类能力,缩短了模型训练时间.

Multi-label remote sensing image classification based on graph convolutional network

A single semantic category label cannot describe comprehensively the image content because remote sensing images contain various object categories. The task of multi-label image classification is more challenging. By exploring the depth graph convolutional network(GCN), this paper made up for the lack of relevance of label semantic information in multi-label remote sensing image classification, proposed a new multi-label remote sensing image classification network, multi-label remote sensing image classification network based on the GCN. It contained three parts: image feature learning module, classifiers learning module based on GCN, and image feature differentiating module. Compared with the related models on the public multi label remote sensing datasets planet and UCM, the method can get better classification results on the multi label remote sensing image classification task. The method uses modules such as graph convolution to apply multi-label image classification methods to remote sensing, which improves the model classification ability and shortens the model training time.