Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China.
2017 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 55, no 6, 3115-3131 p.Article in journal (Refereed) Published
Abstract [en]

Simple linear iterative clustering (SLIC) algorithm was proposed for superpixel generation on optical images and showed promising performance. Several studies have been proposed to modify SLIC to make it applicable for polarimetric synthetic aperture radar (PolSAR) images, where the Wishart distance is adopted as the similarity measure. However, the superpixel segmentation results of these methods were not satisfactory in heterogeneous urban areas. Further, it is difficult to determine the tradeoff factor which controls the relative weight between polarimetric similarity and spatial proximity. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel generation method is proposed to overcome these limitations. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel generation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed superpixel generation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 55, no 6, 3115-3131 p.
Keyword [en]
Spherically invariant random vector (SIRV), superpixel, edge detection, Simple linear iterative clustering (SLIC), polarimetric SAR (PolSAR).
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-187944DOI: 10.1109/TGRS.2017.2662010ISI: 000402063500005OAI: oai:DiVA.org:kth-187944DiVA: diva2:932590
Note

QC 20160607

Available from: 2016-06-01 Created: 2016-06-01 Last updated: 2017-06-19Bibliographically approved
In thesis
1. Urban Area Information Extraction From Polarimetric SAR Data
Open this publication in new window or tab >>Urban Area Information Extraction From Polarimetric SAR Data
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Polarimetric Synthetic Aperture Radar (PolSAR) has been used for various remote sensing applications since more information could be obtained in multiple polarizations. The overall objective of this thesis is to investigate urban area information extraction from PolSAR data with the following specific objectives: (1) to exploit polarimetric scattering model-based decomposition methods for urban areas, (2) to investigate effective methods for man-made target detection, (3) to develop edge detection and superpixel generation methods, and (4) to investigate urban area classification and segmentation.

Paper 1 proposes a new scattering coherency matrix to model the cross-polarized scattering component from urban areas, which adaptively considers the polarization orientation angles of buildings. Thus, the HV scattering components from forests and oriented urban areas can be modelled respectively. Paper 2 presents two urban area decompositions using this scattering model. After the decomposition, urban scattering components can be effectively extracted.

Paper 3 presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Reflection asymmetry was incorporate into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets.

In Paper 4, the edge detection of PolSAR data was investigated using SIRV model and Gauss-shaped filter. This detector can locate the edge pixels accurately with fewer omissions. This could be useful for speckle noise reduction, superpixel generation and others.

Paper 5 investigates an unsupervised classification method for PolSAR data in urban areas. The ortho and oriented buildings can be discriminated very well. Paper 6 proposes an adaptive superpixel generation method for PolSAR images. The algorithm produces compact superpixels that can well adhere to image boundaries in both natural and urban areas.

Abstract [sv]

Polarimetriska Synthetic Aperture Radar (PolSAR) har använts för olika fjärranalystillämpningar för, eftersom mer information kan erhållas från multipolarisad data. Det övergripande syftet med denna avhandling är att undersöka informationshämtning över urbana områden från PolSAR data med följande särskilda mål: (1) att utnyttja polarimetrisk spridningsmodellbaserade nedbrytningsmetoder för stadsområden, (2) att undersöka effektiva metoder för upptäckt av konstgjorda objekt, (3) att utveckla metoder som kantavkänning och superpixel generation, och (4) för att undersöka klassificering och segmentering av stadsområden.

Artikel 1 föreslår en ny spridnings-koherens matris för att modellera korspolariserade spridningskomponent från tätorter, som adaptivt utvärderar polariseringsorienteringsvinkel av byggnader. Artikel 2 presenterar nedbrytningstekniken över två urbana områden med hjälp av denna spridningsmodell. Efter nedbrytningen kunde urbana spridningskomponenter effektivt extraheras.

Artikel 3 presenterar en förbättrad detekteringsmetod för konstgjorda mål med PolSAR data baserade på icke-stationaritet och asymmetri. integrerades reflektionsasymmetri i icke-stationaritetsmetoden för att förbättra noggrannheten i upptäckten av konstgjorda föremål, dvs. att ta bort naturområden och upptäcka de små föremålen.

I artikel 4 undersöktes kantdetektering av PolSAR data med hjälp av SIRV modell och ett Gauss-formad filter. Denna detektor kan hitta kantpixlarna noggrant med mindre utelämnande. Detta skulle den vara användbar för reduktion av brus, superpixel generation och andra.

Artikel 5 utforskar en oövervakad klassificeringsmetod av PolSAR data över stadsområden. Orto- och orienterade byggnader kan särskiljas mycket väl. Baserat på artikel 4 föreslår artikel 6 en adaptiv superpixel generationensmetod för PolSAR data. Algoritmen producerar kompakta superpixels som kan kommer att följa bildgränser i både naturliga och stadsområden.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 121 p.
Series
TRITA-SOM, ISSN 1653-6126
Keyword
Polarimetric SAR, Scattering Decomposition, Man-Made Target Detection, Edge Detection, Superpixel, Urban Classification, Polarimetrisk SAR, Spridningsnedbrytning, Upptäckt av artificiella objekt, Kantupptäckt, Superpixel, Urban klassificering
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-187951 (URN)978-91-7729-047-6 (ISBN)
Public defence
2016-08-25, Kollegiesalen, Brinellvägen 8, KTH-Campus, Stockholm, 13:30 (English)
Opponent
Supervisors
Note

QC 20160607

Available from: 2016-06-07 Created: 2016-06-01 Last updated: 2016-06-07Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Xiang, DeliangBan, YifangWang, Wei
By organisation
Geoinformatics
In the same journal
IEEE Transactions on Geoscience and Remote Sensing
Remote Sensing

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 611 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf