Work

Hypergraph $p$ -Laplacian Regularization for Remotely Sensed Image Recognition

Year: 2018

Type: article

Abstract: Graph-based and manifold-regularization (MR)-based semisupervised learning, including Laplacian regularization (LapR) and hypergraph LapR (HLapR), have achieved prominent performance in preserving locality and similarity information. However, it is still a great challenge to exactly explore and exploit the local structure of the data distribution. In this paper, we present an efficient and effective approximation algorithm of hypergraph${p}$-Laplacian and then propose hypergraph${p}$-LapR (HpLapR) to preserve the geometry of the probability distribution. In particular, hypergraph is a generalization of a standard graph while hypergraph${p}$-Laplacian is a nonlinear generalization of the standard graph Laplacian. The proposed HpLapR shows great potential to exploit the local structures. We integrate HpLapR with logistic regression for remote sensing image recognition. Experiments on UC-Merced data set demonstrate that the proposed HpLapR has superior performance compared with several popular MR methods including LapR and HLapR.

Source: IEEE Transactions on Geoscience and Remote Sensing

DOI: 10.1109/tgrs.2018.2867570

Authors: Xueqi Ma, Weifeng Liu, Shuying Li, Dapeng Tao, Yicong Zhou

Institutions: China University of Petroleum, East China, Xi’an University of Posts and Telecommunications, Yunnan University, University of Macau

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Yunnan University, Government of Shandong Province