Decision Fusion for Reliable Flood Mapping Using Remote Sensing Images

Dey, Chandrama; Jia, Xiuping; Fraser, Donald

doi:10.1109/dicta.2008.65

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…Hess et al 1995, Tholey et al 1997, Brakenridge et al 1998, Townsend and Walsh 1998, Bourgeau-Chavez et al 2001, Townsend 2002, Wang 2002, Ahtonen et al 2005, Dey et al 2008, Mason et al 2009, Ramsey et al 2009, Schumann et al 2009, Bwangoy et al 2010. Typically, during-event SAR images and pre-event optical images are fused to produce a flood inundation map.…”

Section: Fusion Of Sar and Optical Images To Map Flood Inundationmentioning

confidence: 97%

Radar image and data fusion for natural hazards characterisation

Lü

Dzurisin

Jung

et al. 2010

International Journal of Image and Data Fusion

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Section: Fusion Of Sar and Optical Images To Map Flood Inundationmentioning

confidence: 97%

Radar image and data fusion for natural hazards characterisation

Lü

Dzurisin

Jung

et al. 2010

International Journal of Image and Data Fusion

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“…Brivio et al (2002) used topographic data in combination with radar data to extract flooded areas at their peak. Dey et al (2008) considered a decision fusion approach to combine classification results from Radarsat and Landsat data to distinguish between permanent water and flood regions. Li and Chen (2005) applied a decision rule technique using Radarsat 1, Landsat 7 and DEM data for wetlands mapping.…”

Section: Introductionmentioning

confidence: 99%

A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico

Chaouch

Temimi

Hagen

et al. 2011

Hydrological Processes

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Abstract:This work proposes a method for detecting inundation between semi-diurnal low and high water conditions in the northern Gulf of Mexico using high-resolution satellite imagery. Radarsat 1, Landsat imagery and aerial photography from the Apalachicola region in Florida were used to demonstrate and validate the algorithm. A change detection approach was implemented through the analysis of red, green and blue (RGB) false colour composites image to emphasise differences in high and low tide inundation patterns. To alleviate the effect of inherent speckle in the SAR images, we also applied ancillary optical data. The flood-prone area for the site was delineated a priori through the determination of lower and higher water contour lines with Landsat images combined with a high-resolution digital elevation model. This masking technique improved the performance of the proposed algorithm with respect to detection techniques using the entire Radarsat scene. The resulting inundation maps agreed well with historical aerial photography as the probability of detection reached 83%. The combination of SAR data and optical images, when coupled with a high-resolution digital elevation model, was shown to be useful for inundation mapping and have a great potential for evaluating wetting/drying algorithms of inland and coastal hydrodynamic models.

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“…Optical remote sensing analysis using machine learning Remote sensing imagery analysis using machine learning techniques is a broad area in academic literature with much research attention dedicated to it. Most works explore natural remote sensing imagery [10], [12], [39], while some works explore abstractions of remote sensing imagery within the context of machine learning [28], [35], [43]. Methods in these areas almost exclusively follow supervised or unsupervised approaches, with very few, such as [34], following self-supervised approaches.…”

Section: Related Workmentioning

confidence: 99%

Self-Supervision, Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations

Seneviratne,

Nice,

Wijnands

et al. 2022

Preprint

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Self-supervision based deep learning classification approaches have received considerable attention in academic literature. However, the performance of such methods on remote sensing imagery domains remains under-explored. In this work, we explore contrastive representation learning methods on the task of imagery-based city classification, an important problem in urban computing. We use satellite and map imagery across 2 domains, 3 million locations and more than 1500 cities. We show that self-supervised methods can build a generalizable representation from as few as 200 cities, with representations achieving over 95% accuracy in unseen cities with minimal additional training. We also find that the performance discrepancy of such methods, when compared to supervised methods, induced by the domain discrepancy between natural imagery and abstract imagery is significant for remote sensing imagery. We compare all analysis against existing supervised models from academic literature and open-source our models 1 for broader usage and further criticism.

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Decision Fusion for Reliable Flood Mapping Using Remote Sensing Images

Cited by 8 publications

References 5 publications

Radar image and data fusion for natural hazards characterisation

Radar image and data fusion for natural hazards characterisation

A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico

Self-Supervision, Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations

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