Object-Based Classification Framework of Remote Sensing Images With Graph Convolutional Networks

DOI码：10.1109/lgrs.2021.3072627

发表刊物：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

关键字：Feature extraction,Image segmentation,Data models,Sparse matrices,Remote sensing,Chebyshev approximation,Training,Deep learning (DL),graph convolutional network (GCN),object-based image classification (OBIC),remote sensing (RS)

摘要：Object-based image classification (OBIC) on very-high-resolution (VHR) remote sensing (RS) images is utilized in a wide range of applications. Nowadays, many existing OBIC methods only focus on features of each object itself, neglecting the contextual information among adjacent objects and resulting in low classification accuracy. Inspired by a spectral graph theory, we construct a graph structure from objects generated from VHR RS images and propose an OBIC framework based on truncated sparse singular value decomposition and graph convolutional network (GCN) model, aiming to make full use of relativities among objects and produce an accurate classification. Through conducting experiments on two annotated RS image data sets, our framework obtained 97.2% and 66.9% overall accuracy, respectively, in automatic and manual object segmentation circumstances, within a processing time of about 1/100 of convolutional neural network (CNN)-based methods' training time.

合写作者：Xiaoliang Tan,Kun Zhu,Puyun Liao,Tong Wang

论文类型：期刊论文

通讯作者：Guanzhou Chen

文献类型：J

卷号：19

ISSN号：1545-598X

是否译文：否

发表时间：2021-04-22

收录刊物：SCI