NDLI: Semi-supervised classification based on anchor-spatial graph for large polarimetric SAR data

Content Provider	IEEE Xplore Digital Library
Author	Hongying Liu Yikai Wang Dexiang Zhu Shuyuan Yang Shuang Wang Biao Hou Licheng Jiao
Copyright Year	2015
Description	Author affiliation: Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China (Hongying Liu; Yikai Wang; Dexiang Zhu; Shuyuan Yang; Shuang Wang; Biao Hou; Licheng Jiao)
Abstract	Recently a few works of semi-supervised learning methods based on graph have been proposed for remote sensing. The common idea of these methods are that they build a graph using the samples of the image. Most of their time complexity is relatively large, and they ignore the spatial information of the image, which leads to unsatisfactory classification results. this paper proposes a novel semi-supervised classification method based on anchor-spatial graph for large PolSAR data. Firstly the unsupervised Wishart clustering is performed to select representative samples, which served as anchors according to the least distance between samples. Then an anchor graph is built using the selected anchors according to the multiple features of the samples. And it is further combined with the spatial information of the samples to construct an anchor-spatial graph. Finally the class information from small quantities of labeled samples propagates to the unlabeled ones. Experimental results show that the proposed method has a low time complexity compared with existing works and it could effectively cut down the processing time for large PolSAR data meanwhile keeps the classification accuracy.
Starting Page	1845
Ending Page	1848
File Size	373686
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326151
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Sparse matrices Time complexity Synthetic aperture radar Classification algorithms Kernel Semisupervised learning anchor-spatial graph semi-supervised learning methods based on graph
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Semi-supervised learning for classification of polarimetric SAR-data

Informativity-based graph: Exploring mutual kNN and labeled vertices for semi-supervised learning

A semisupervised contextual classification algorithm for multitemporal polarimetric SAR data

Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method

Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method

Parameters analysis for polarimetric SAR Based on classification accuracy

Polarmetric SAR images classification based on sparse representation theory

Label propagation and soft-similarity measure for graph based Constrained Semi-Supervised Learning

A new method for polarimetric SAR image classification

Semi-supervised classification based on anchor-spatial graph for large polarimetric SAR data

Similar Documents

Semi-supervised learning for classification of polarimetric SAR-data

Informativity-based graph: Exploring mutual kNN and labeled vertices for semi-supervised learning

A semisupervised contextual classification algorithm for multitemporal polarimetric SAR data

Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method

Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method

Parameters analysis for polarimetric SAR Based on classification accuracy

Polarmetric SAR images classification based on sparse representation theory

Label propagation and soft-similarity measure for graph based Constrained Semi-Supervised Learning

A new method for polarimetric SAR image classification

Semi-supervised classification based on anchor-spatial graph for large polarimetric SAR data