Remote sensing has been widely used in disaster management. However, application of optical imageries in damage detection is not always feasible for immediate damage assessment. In the case of the Sichuan earthquake in 2008, the damaged areas were covered by cloud and fog for most of the time. The all weather SAR imageries could instead provide information of the damaged area. Therefore, more efforts are needed to explore the usability of SAR data. In regards to this purpose, this research focuses on studying the ability of using various SAR data in damage identification through image classification, and furthermore the effectiveness of fusion of various sensors in classification is evaluated. Three different types of SAR imagery were acquired over the heavily damaged zone Qushan town in the Sichuan earthquake. The 3 types of SAR data are ALOS PALSAR L-band, RADARSAT-1 C-band and the TerraSAR-X X- band imageries. Maximum likelihood classification method is applied on the imageries. Four classes: Water, collapsed area, built-up area and landslide area are defined in the study area. The ability of each band in identifying these four classes is studied and the overall classification accuracy is analysed. Furthermore, fusion of these 3 types of imageries is performed and the effectiveness and accuracy of image fusion classification are evaluated. The results show that classification accuracy from individual SAR imagery is not ideal. The overall accuracy which PALSAR gives is 30.383%, RADARSAT-1 is 31.268% while TerraSAR-X only achieves 37.168%. Accuracy statistics demonstrate that TerraSAR-X performs the best in classifying these four classes. SAR image fusion shows a better classification result. Double image fusion of PALSAR and RADARSAT-1, PALSAR and TerraSAR-X, and RADARSAT-1 and TerraSAR-X give an overall classification accuracy of 41.88%, 42.478% and 37.758% respectively. The result from triple image fusion even reaches 52.507%. They are all higher than the result given by the individual images. The study illustrates that the VHR TerraSAR X band SAR data has a higher ability in classification of damages, and fusion of different band can improve the classification accuracy.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-37005 |
| Date | January 2011 |
| Creators | LAU, SIN WAI |
| Publisher | KTH, Geodesi och geoinformatik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-GIT EX, ISSN 1653-5227 ; 11-003 |
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