A semisupervised contextual classification algorithm for multitemporal polarimetric SAR data
2012 (English)In: Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, IEEE , 2012, 1777-1780 p.Conference paper (Refereed)
This paper presents a contextual classification algorithm which employs the multiscale modified Pappas adaptive clustering (MPAC) approach and the Semisupervised Expectation-Maximization (SEM) procedure for urban land cover mapping using multitemporal polarimetric SAR (PolSAR) data. The proposed pixel-based algorithm explores spatio-temporal contextual information and thus could effectively improve the classification accuracy while simultaneously avoids the pepper-salt results which often occurs on the SAR images. Moreover, owing to the multiscale analysis, MPAC could adaptively preserve the detailed features comparing with other non-adaptive contextual methods. The proposed algorithm is computationally efficient and requires less parameter to be estimated. Properties of the proposed algorithm including the MRF impact, multiscale efficiency, computational performance and the initialization influence were investigated. Six-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used for validation. The results show that this algorithm could generate homogenous and detailed mapping results with fair accuracy for complex urban land cover classification.
Place, publisher, year, edition, pages
IEEE , 2012. 1777-1780 p.
, IEEE International Symposium on Geoscience and Remote Sensing IGARSS, ISSN 2153-6996
MPAC, Polarimetric SAR, SEM, Urban mapping
IdentifiersURN: urn:nbn:se:kth:diva-118398DOI: 10.1109/IGARSS.2012.6351171ISI: 000313189401247ScopusID: 2-s2.0-84873127054ISBN: 978-1-4673-1159-5OAI: oai:DiVA.org:kth-118398DiVA: diva2:606064
2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, 22 July 2012 through 27 July 2012, Munich
QC 201302182013-02-182013-02-182013-03-15Bibliographically approved