| 1. |
- Ban, Yifang, et al.
(författare)
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EO4Urban : Sentinel-1A SAR and Sentinel-2A MSI data for global urban services
- 2017
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Ingår i: 2017 Joint Urban Remote Sensing Event, JURSE 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509058082
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Konferensbidrag (refereegranskat)abstract
- The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Ten cities around the world in different geographical and environmental conditions were selected as study areas. Large volumes of Sentinel-1A SAR and Sentinel-2A MSI data were acquired during the vegetation season in 2015 and 2016. The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal Sentinel-1 SAR, Sentinel-2A MSI data and their fusion using the Urban Extractors developed within the project. For urban land cover mapping, multitemporal Sentinel-1A SAR data alone yielded an overall classification accuracy of 60% for Stockholm. Sentinel-2A MSI data as well as the fusion of Sentinel-1A SAR and Sentinel-2A MSI data, however, produced much higher classification accuracies, both reached 80%.
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| 2. |
- Mitraka, Zina, et al.
(författare)
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Towards discriminating between zones with different thermal behaviour in cities
- 2017
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Ingår i: 2017 Joint Urban Remote Sensing Event, JURSE 2017. Dubai, United Arab Emirates; 6-8 March 2017.. - 9781509058082
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Konferensbidrag (refereegranskat)abstract
- The advances in satellite sensor technology, the launch of new satellites and the continuous development of remote sensing technology constantly increase the opportunities for monitoring the thermal behaviour of cities from space. Not only the surface temperature, but several other parameters related to the urban climate can be quantified from Earth Observation (EO) data. In this study, remote sensing techniques are applied to derive EO data products and a methodology is proposed that combines this information to discriminate between zones with different thermal behaviour in cities. Information on the building, pervious and paved surface cover, the surface albedo, the mean building/tree height and the sky view factor is quantified in local scale and then used to identify possible zones with homogeneous thermal characteristics.
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| 3. |
- Yousif, Osama, et al.
(författare)
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Fusion of SAR and optical data for unsupervised change detection : A case study in Beijing
- 2017
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Ingår i: 2017 Joint Urban Remote Sensing Event, JURSE 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509058082
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Konferensbidrag (refereegranskat)abstract
- Change detection can either be carried out using multitemporal optical or synthetic aperture radar (SAR) images. Due to the different electromagnetic spectrum used, these two types of imagery provide different representations of the same physical reality. Change information extraction can benefit from the fusion of SAR and optical data. In this paper we investigate the fusion of SAR and optical for change detection application. Beijing, the capital of China that has experienced rapid urbanization, is selected as a case study. Two multitemporal datasets that consist of Landsat and SAR (ERS-2 and ENVISAT) images are used. An unsupervised classification framework that combines the virtues of the k-mean and SVM supervised classifier is proposed. Different fusion strategies are tested including fusion at the feature level and at the decision level. The analysis reveals that the best result can be obtained when the fusion of change information is carried out at the decision level.
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