DOI number:10.1109/lgrs.2021.3072627

Journal:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

Key Words: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)

Abstract: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.

Co-author:Xiaoliang Tan,Kun Zhu,Puyun Liao,Tong Wang

Indexed by:Journal paper

Correspondence Author:Guanzhou Chen

Document Type:J

Volume:19

ISSN No.:1545-598X

Translation or Not:no

Date of Publication:2021-04-22

Included Journals:SCI