Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images
作者机构：School of Information Science&EngineeringShandong Agricultural UniversityTaian271018China
出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)
年 卷 期：2023年第37卷第7期
页 面：491-506页
核心收录：
学科分类：0810[工学-信息与通信工程] 08[工学] 081002[工学-信号与信息处理]
基 金：funded by the Major Scientific and Technological Innovation Project of Shandong Province Grant No.2022CXGC010609
主 题：Remote sensing image semantic segmentation GCN spatial relationship feature fusion
摘 要：Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image *** the continuous develop-ment of artificial intelligence technology,the use of deep learning methods for interpreting remote-sensing images has *** neural networks disregard the spatial relationship between two targets in remote sensing *** segmentation models that combine convolutional neural networks(CNNs)and graph convolutional neural networks(GCNs)cause a lack of feature boundaries,which leads to the unsatisfactory segmentation of various target feature *** this paper,we propose a new semantic segmentation model for remote sensing images(called DGCN hereinafter),which combines deep semantic segmentation networks(DSSN)and *** the GCN module,a loss function for boundary information is employed to optimize the learning of spatial relationship features between the target features and their relationships.A hierarchical fusion method is utilized for feature fusion and classification to optimize the spatial relationship informa-tion in the original feature *** experiments on ISPRS 2D and DeepGlobe semantic segmentation datasets show that compared with the existing semantic segmentation models of remote sensing images,the DGCN significantly optimizes the segmentation effect of feature boundaries,effectively reduces the noise in the segmentation results and improves the segmentation accuracy,which demonstrates the advancements of our model.
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