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Urban Area Information Extraction From Polarimetric SAR Data
KTH, School of Architecture and the Built Environment (ABE). (Division of Geoinformatics)
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 [en]
Polarimetric SAR, Scattering Decomposition, Man-Made Target Detection, Edge Detection, Superpixel, Urban Classification
Keyword [sv]
Polarimetrisk SAR, Spridningsnedbrytning, Upptäckt av artificiella objekt, Kantupptäckt, Superpixel, Urban klassificering
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-187951ISBN: 978-91-7729-047-6 (print)OAI: oai:DiVA.org:kth-187951DiVA: diva2:933073
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
List of papers
1. Unsupervised polarimetric SAR urban area classification based on model-based decomposition with cross scattering
Open this publication in new window or tab >>Unsupervised polarimetric SAR urban area classification based on model-based decomposition with cross scattering
Show others...
2016 (English)In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 116, 86-100 p.Article in journal (Refereed) Published
Abstract [en]

Since it has been validated that cross-polarized scattering (HV) is caused not only by vegetation but also by rotated dihedrals, in this study, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended four-component model-based decomposition method for PolSAR data over urban areas. Unlike other urban area decomposition techniques which need to discriminate the urban and natural areas before decomposition, this proposed method is applied on PolSAR image directly. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition. Therefore, we can separate cross scattering of urban areas from the overall HV component. Further, the cross and helix scattering components are also compared. Then, using these decomposed scattering powers, the buildings and natural areas can be easily discriminated from each other using a simple unsupervised K-means classifier. Moreover, buildings aligned and not aligned along the radar flight direction can be also distinguished clearly. Spaceborne RADARSAT-2 and airborne AIRSAR full polarimetric SAR data are used to validate the performance of our proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other state-of-the-art urban decomposition techniques. The decomposed scattering powers significantly improve the classification accuracy for urban areas.

Place, publisher, year, edition, pages
Elsevier, 2016
Keyword
Cross scattering matrix, K-means classifier, Model-based decomposition, Polarimetric SAR (PolSAR), Urban area classification, Matrix algebra, Polarimeters, Satellite communication systems, K-means, Model based decompositions, Polarimetric SAR, Scattering matrices, Synthetic aperture radar
National Category
Earth and Related Environmental Sciences Communication Systems
Identifiers
urn:nbn:se:kth:diva-186934 (URN)10.1016/j.isprsjprs.2016.03.009 (DOI)2-s2.0-84962538521 (Scopus ID)
Note

QC 20160524

Available from: 2016-05-24 Created: 2016-05-16 Last updated: 2017-11-30Bibliographically approved
2. Man-Made Target Detection from Polarimetric SAR Data via Nonstationarity and Asymmetry
Open this publication in new window or tab >>Man-Made Target Detection from Polarimetric SAR Data via Nonstationarity and Asymmetry
2016 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 9, no 4, 1459-1469 p., 7405260Article in journal (Refereed) Published
Abstract [en]

Detection of man-made targets in urban areas using polarimetric synthetic aperture radar (PolSAR) data has become a promising research area since it has a close relationship with urban planning, rescue service, etc. This paper presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Nonstationarity in azimuth direction is already utilized to separate man-made and natural targets in urban areas. However, there are still some drawbacks. Some small man-made targets and roads cannot be effectively detected. In addition, nonstationarity can also occur in some other natural surfaces, such as cropland with Bragg resonance. Therefore, to resolve these problems, we incorporate reflection asymmetry 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. Airborne ESAR data and spaceborne PALSAR data are used to validate the performance of the proposed method. The result obtained by our proposed method shows a 20% higher accuracy than the result based on original nonstationarity extraction method. Natural areas with Bragg resonance are removed. Moreover, most of the buildings and some metallic fences along the road can also be accurately detected.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keyword
Man-made target detection, nonstationarity, polarimetric SAR (PolSAR), reflection asymmetry
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-187179 (URN)10.1109/JSTARS.2016.2520518 (DOI)000375868800011 ()2-s2.0-84959153263 (Scopus ID)
Note

QC 20160518

Available from: 2016-05-18 Created: 2016-05-18 Last updated: 2017-11-30Bibliographically approved
3. Model-Based Decomposition With Cross Scattering for Polarimetric SAR Urban Areas
Open this publication in new window or tab >>Model-Based Decomposition With Cross Scattering for Polarimetric SAR Urban Areas
2015 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 12, no 12, 2496-2500 p.Article in journal (Refereed) Published
Abstract [en]

Cross-polarized scattering (HV) is not only caused by vegetation but also by rotated dihedrals. In this letter, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended model-based decomposition method for polarimetric synthetic aperture radar (PolSAR) data over urban areas. Unlike other urban decomposition techniques which need to discriminate between urban and natural areas before decomposition, this proposed method is applied directly on the PolSAR image. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition process. This enables the separation of the cross scattering of urban areas from the overall HV component. The cross and helix scattering components are also compared in this study. RADARSAT-2 quad-pol C band and AIRSAR L band data are used to validate the performance of the proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other urban decomposition techniques.

Place, publisher, year, edition, pages
IEEE Press, 2015
Keyword
Building orientation angle, cross scattering matrix, model-based decomposition, polarimetric SAR (PolSAR), urban areas
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-179583 (URN)10.1109/LGRS.2015.2487450 (DOI)000364993500029 ()2-s2.0-84947865089 (Scopus ID)
Note

QC 20160112

Available from: 2016-01-12 Created: 2015-12-17 Last updated: 2017-11-30Bibliographically approved
4. Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model
Open this publication in new window or tab >>Adaptive Superpixel Generation for Polarimetric SAR Images With Local Iterative Clustering and SIRV Model
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
Keyword
Spherically invariant random vector (SIRV), superpixel, edge detection, Simple linear iterative clustering (SLIC), polarimetric SAR (PolSAR).
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-187944 (URN)10.1109/TGRS.2017.2662010 (DOI)000402063500005 ()
Note

QC 20160607

Available from: 2016-06-01 Created: 2016-06-01 Last updated: 2017-11-22Bibliographically approved
5. Edge Detector for Polarimetric SAR ImagesUsing SIRV Model and Gauss-Shaped Filter
Open this publication in new window or tab >>Edge Detector for Polarimetric SAR ImagesUsing SIRV Model and Gauss-Shaped Filter
(English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571Article in journal (Refereed) Submitted
Abstract [en]

The classic constant false alarm rate (CFAR) edgedetector with rectangle-shaped filter has been proven to beeffective and widely used in polarimetric SAR (PolSAR) images.However, in practical use, the assumption of complex Wishartdistribution is often not respected, especially in heterogeneousurban areas. In addition, as a simple smoothing filter, therectangle-shaped window is often shown to be easy to incur falseedge pixels near true edges. Therefore, its performance islimited. To overcome this restriction, we propose a new edgedetector for PolSAR images, which utilizes the sphericallyinvariant random vector (SIRV) product model to estimate thenormalized covariance matrix for each pixel and then replacethe rectangle-shaped filter with Gauss-shaped filter. Theperformance of our proposed methodology is presented andanalyzed on two real PolSAR data sets, and the results show thatthe new edge detector attains better performance than theclassic one, particularly for urban areas.

Place, publisher, year, edition, pages
IEEE Press
Keyword
Spherically invariant random vector (SIRV), urban areas, edge detection, Gauss-shaped filter, polarimetric SAR (PolSAR).
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-187943 (URN)
Note

QC 20160607

Available from: 2016-06-01 Created: 2016-06-01 Last updated: 2017-11-30Bibliographically approved
6. The cross-scattering component of polarimetric SAR in urban areasand its application to model-based scattering decomposition
Open this publication in new window or tab >>The cross-scattering component of polarimetric SAR in urban areasand its application to model-based scattering decomposition
(English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901Article in journal (Refereed) Accepted
Abstract [en]

After the work of Freeman, Durden, Pottier, and Yamaguchi, manydecomposition techniques have been proposed for urban areas, mainly to resolvethe overestimation problem of volume scattering. Since it has been validated thatthe cross-polarised (HV) scattering is caused not only by forests but also byrotated dihedrals, in this paper, we propose a cross scattering coherency matrix tomodel the HV component from orientated and complex buildings and thendemonstrate its performance on model-based scattering decomposition. Thebuilding orientation angle is considered in this coherency matrix, making itflexible and adaptive in the decomposition. Therefore, the HV components fromforests and orientated urban areas can be modelled respectively. Twodecomposition procedures are applied in this paper. The first one is to validatethe effectiveness of this scattering model. We regard the HV component fromurban areas as cross scattering, which is an independent scattering componentadded to the Yamaguchi four-component decomposition. Another one is theurban area decomposition application using this scattering model. Decompositionis implemented for urban and natural areas respectively and the HV componentfrom urban areas is regarded as their volume scattering. This procedure is similarto many other state-of-the-art methods for urban areas and needs to discriminatethe urban and natural areas before decomposition. Spaceborne Radarsat-2 C band,the Airborne Synthetic Aperture Radar (AIRSAR) L band and UninhabitedAerial Vehicle Synthetic Aperture Radar (UAVSAR) L band full polarimetricSAR data are used to validate the performance of this cross scattering coherencymatrix. The HV component of orientated buildings is generated, leading to abetter decomposition result for urban areas.

Place, publisher, year, edition, pages
Taylor & Francis Group
Keyword
cross scattering coherency matrix; orientated urban areas; modelbased decomposition; polarimetric SAR (PolSAR)
National Category
Remote Sensing
Identifiers
urn:nbn:se:kth:diva-187939 (URN)
Note

QC 20160607

Available from: 2016-06-01 Created: 2016-06-01 Last updated: 2017-11-30Bibliographically approved

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