Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Multitemporal Remote Sensing for Urban Mapping using KTH-SEG and KTH-Pavia Urban Extractor
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geodesy and Geoinformatics.ORCID iD: 0000-0003-4434-7244
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The objective of this licentiate thesis is to develop novel algorithms and improve existing methods for urban land cover mapping and urban extent extraction using multi-temporal remote sensing imagery. Past studies have demonstrated that synthetic aperture radar (SAR) have very good properties for the analysis of urban areas, the synergy of SAR and optical data is advantageous for various applications. The specific objectives of this research are:

1. To develop a novel edge-aware region-growing and -merging algorithm, KTH-SEG, for effective segmentation of SAR and optical data for urban land cover mapping;

2. To evaluate the synergistic effects of multi-temporal ENVISAT ASAR and HJ-1B multi-spectral data for urban land cover mapping;

3. To improve the robustness of an existing method for urban extent extraction by adding effective pre- and post-processing.

ENVISAT ASAR data and the Chinese HJ-1B multispectral , as well as TerraSAR-X data were used in this research. For objectives 1 and 2 two main study areas were chosen, Beijing and Shanghai, China. For both sites a number of multitemporal ENVISAT ASAR (30m C-band) scenes with varying image characteristics were selected during the vegetated season of 2009. For Shanghai TerraSAR-X strip-map images at 3m resolution X-band) were acquired for a similar period in 2010 to also evaluate high resolution X-band SAR for urban land cover mapping. Ten  major landcover classes were extracted including high density built-up, low density built-up, bare field, low vegetation, forest, golf course, grass, water, airport runway and major road.

For Objective 3, eleven globally distributed study areas where chosen, Berlin, Beijing, Jakarta, Lagos, Lombardia (northern Italy), Mexico City, Mumbai, New York City, Rio de Janeiro, Stockholm and Sydney. For all cities ENVISAT ASAR imagery was acquired and for cities in or close to mountains even SRTM digital elevation data.

The methodology of this thesis includes two major components, KTH-SEG and KTH-Pavia Urban Extractor. KTH-SEG is an edge aware region-growing and -merging algorithm that utilizes both the benefit of finding local high frequency changes as well as determining robustly homogeneous areas of a low frequency in local change. The post-segmentation classification is performed using support vector machines. KTH-SEG was evaluated using multitemporal, multi-angle, dual-polarization ASAR data and multispectral HJ-1B data as well as TerraSAR-X data. The KTH-Pavia urban extractor is a processing chain. It includes: Geometrical corrections, contrast enhancement, builtup area extraction using spatial stastistics and GLCM texture features, logical operator based fusion and DEM based mountain masking.

For urban land cover classification using multitemporal ENVISAT ASAR data, the results showed that KTH-SEG achieved an overall accuracy of almost 80% (0.77 Kappa ) for the 10 urban land cover classes both Beijign and Shanghai, compared to eCognition results of 75% (0.71 Kappa) In particular the detection of small linear features with respect to the image resolution such as roads in 30m resolved data went well with 83% user accuracy from KTH-SEG versus 57% user accuracy using the segments derived from eCognition. The other urban classes which in particular in SAR imagery are characterized by a high degree of heterogeneity were classified superiorly by KTH-SEG. ECognition in general performed better on vegetation classes such as grass, low vegetation and forest which are usually more homogeneous.

It is was also found that the combination of ASAR and HJ-1B optical data was beneficial, increasing the final classification accuracy by at least 10% compared to ASAR or HJ-1B data alone. The results also further confirmed that a higher diversity of SAR type images is more important for the urban classification outcome. However, this is not the case when classifying high resolution TerraSAR-X strip-map imagery. Here the different image characteristics of different look angles, and orbit orientation created more confusion mainly due to the different layover and foreshortening effects on larger buildings. The TerraSAR-X results showed also that accurate urban classification can be achieved using high resolution SAR data alone with almost 84% for  eight classes around the Shanghai international Airport (high and low density built-up were not separated as well as roads and runways).

For urban extent extraction, the results demonstrated that built-up areas can be effectively extracted using a single ENVISAT ASAR image in 10 global cities reaching overall accuracies around 85%, compared to 75% of MODIS urban class and 73% GlobCover Urban class. Multitemporal ASAR can improve the urban extraction results by 5-10% in Beijing. Mountain masking applied in Mumbai and Rio de Janeiro increased the accuracy by 3-5%.The research performed in  this thesis has contributed to the remote sensing community by providing algorithms and methods for both extracting urban areas and identifying urban land cover in a more detailed fashion. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. , ix, 51 p.
Series
TRITA-SOM, ISSN 1653-6126 ; 2014:08
Keyword [en]
KTH-SEG, ASAR, HJ-1B, Urban Land Cover Mapping, OBIA, Segmentation, Image Classification, KTH-Pavia Urban Extractor, Urban Extent
National Category
Other Computer and Information Science
Research subject
Geodesy and Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-147159ISBN: 978-91-7595-188-1 (print)OAI: oai:DiVA.org:kth-147159DiVA: diva2:727766
Presentation
2014-06-12, Seminarrum 5055, Drottning Kristinas Väg 30, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20140625

Available from: 2014-06-25 Created: 2014-06-23 Last updated: 2014-06-25Bibliographically approved
List of papers
1. Segmentation of multi-temporal envisat asar and hj-1b optical data using an edge-aware region growing and merging algorithm
Open this publication in new window or tab >>Segmentation of multi-temporal envisat asar and hj-1b optical data using an edge-aware region growing and merging algorithm
2013 (English)In: European Space Agency, (Special Publication) ESA SP, Volume 704 SP, 2013, 2013Conference paper, Published paper (Refereed)
Abstract [en]

The paper aims to develop image segmentation algorithms for classification of multi-sensor data in urban areas. For this purpose an algorithm called KTHSEG has been developed using an edge-aware region growing and merging algorithm. Four-date ENVISAT ASAR C-HH data and one-date HJ-1B covering the city of Shanghai acquired during the vegetation season of 2009 were selected this research. The results show that the segmentation algorithm is effective for urban land cover classification using SAR and optical data. The results also confirm that the fusion of SAR and optical data is beneficial for urban land cover mapping. Further, the study showed that the combination of one SAR and one optical scene is enough to achieve good results and the addition of multitemporal SAR data from the same beam mode does not improve classification accuracy.

Series
European Space Agency, (Special Publication) ESA SP, ISSN 0379-6566 ; 704 SP
Keyword
Classification accuracy, Image segmentation algorithm, Merging algorithms, Multi-sensor data, Multi-temporal SAR, Segmentation algorithms, Urban land cover classification, Urban land cover mappings
National Category
Other Natural Sciences
Identifiers
urn:nbn:se:kth:diva-143843 (URN)2-s2.0-84892745942 (Scopus ID)
Conference
Dragon 2 Final Results and Dragon 3 Kick-Off Symposium; Beijing; China; 25 June 2012 through 29 June 2012
Note

QC 20140415

Available from: 2014-04-15 Created: 2014-03-31 Last updated: 2014-06-25Bibliographically approved
2. Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping
Open this publication in new window or tab >>Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping
2013 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 51, no 4, 1998-2006 p.Article in journal (Refereed) Published
Abstract [en]

The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected.

Keyword
Edge-aware region growing and merging (EARGM), ENVISAT advanced synthetic aperture radar (SAR) (ASAR), fusion, HJ-1B, multitemporal, segmentation, urban land cover
National Category
Geophysics
Identifiers
urn:nbn:se:kth:diva-123430 (URN)10.1109/TGRS.2012.2236560 (DOI)000318426400012 ()2-s2.0-84875675131 (Scopus ID)
Note

QC 20130610

Available from: 2013-06-10 Created: 2013-06-10 Last updated: 2017-12-06Bibliographically approved
3. Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Utilizing a Robust Urban Extractor
Open this publication in new window or tab >>Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Utilizing a Robust Urban Extractor
2015 (English)In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 103Article in journal (Refereed) Published
Abstract [en]

With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrated robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.

Keyword
Spaceborne, SAR, ENVISAT ASAR, Urban Mapping, 30m Resolution, Spatial Indices, GLCM, Textures, Mountain Mask
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-147154 (URN)10.1016/j.isprsjprs.2014.08.004 (DOI)000353734600003 ()
Note

Updated from accepted to published.

QC 20150612

Available from: 2014-06-23 Created: 2014-06-23 Last updated: 2017-12-05Bibliographically approved
4. Urban land cover mapping with TerraSAR-X using an edge-aware region-growing and merging algorithm
Open this publication in new window or tab >>Urban land cover mapping with TerraSAR-X using an edge-aware region-growing and merging algorithm
2014 (English)In: 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE , 2014, 4836-4839 p.Conference paper, Published paper (Refereed)
Abstract [en]

TerraSAR X data has been analyzed for its suitability of urban land cover mapping using our recently developed object based image analysis tool KTH-SEG, which is based on an edge aware region growing and merging algorithm and a support vector machine classifier. Classification results over the Shanghai International Airport area using 8 classes, Water, Grass, Roads, Buildings, Crops, Forest, Bare Crops and Green Houses have proven with an overall accuracy just shy of 84% that this is very well the case. It has further been investigated which segment sizes and image configuration yield the best results.

Place, publisher, year, edition, pages
IEEE, 2014
Series
IEEE International Symposium on Geoscience and Remote Sensing IGARSS, ISSN 2153-6996
Keyword
OBIA, SAR, Urban, Land Cover Mapping, Image Classification
National Category
Other Computer and Information Science
Research subject
Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-147141 (URN)10.1109/IGARSS.2014.6947577 (DOI)000349688106174 ()2-s2.0-84911454836 (Scopus ID)978-1-4799-5775-0 (ISBN)
Conference
Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec Convention Centre Quebec City, Canada, 13 July 2014 through 18 July 2014
Note

QC 20140625

Available from: 2014-06-23 Created: 2014-06-23 Last updated: 2015-04-08Bibliographically approved

Open Access in DiVA

Licentiat Alexander Jacob 2014(2237 kB)343 downloads
File information
File name FULLTEXT01.pdfFile size 2237 kBChecksum SHA-512
877cf489cb15bc1e9e3a861e8473b85c3d8e732a56ae10f593937ce4a413895ae22580e8e5102219eaa91961f6a5732f8db56d73afcff0041406cd0f7b13f0de
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Jacob, Alexander
By organisation
Geodesy and Geoinformatics
Other Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 343 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 953 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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