GB-SAR/PiSAR simultaneous experiment for a trial of flood area detection

Shimada, Masanobu; Matsumoto, Masayoshi; Suzuki, Motoyuki

doi:10.1109/igarss.2008.4779274

Cited by 4 publications

(4 citation statements)

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“…As a consequence, the buildings at the boundary of the flooded zone, where the flood level is lower, should present a brighter double reflection contribution. Moreover, it is worth to mention that the difference between the pre-and post-flood SAR image intensities becomes more remarkable if the acquisition is performed in HH polarization [33].…”

Section: Electromagnetic Backgroundmentioning

confidence: 99%

“…Both the proposed approaches have a general applicability. However, there are some recommended guidelines regarding the SAR data set to which these approaches should be applied: HR single look complex data in HH polarization [33] are highly recommended to better detect double reflection lines; multilooking is unadvisable to avoid blurring effects on the double reflection line; the best buildings to select are those with low orientation angle ϕ in order to have a stronger backscattered electromagnetic field.…”

Section: Conclusion and Future Extensions Of The Approachesmentioning

confidence: 99%

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Flooding Water Depth Estimation With High-Resolution SAR

Iervolino

Guida

Iodice

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Abstract-The retrieval of flooding levels with high-resolution (HR) synthetic aperture radar (SAR) images is presented in this paper. A new framework is proposed. It is based on the inversion of theoretical scattering models initially developed for nonflooded urban areas and here adapted to the flooding case. Starting from the theory, two possible retrieval approaches have been developed and are the main topic of this paper: two possible retrieval approaches have been developed and are the main topic of this paper: the local Single Image Objects Aware (SIObA) and the global Two Image Area Aware (TIArA). These two approaches are conceived to be applicable under different working conditions and consequently holding different properties and reliability. For each of them, a different algorithm is derived and tested, and the retrieval results are validated on a meaningful data set of HR TerraSAR-X images relevant to the Gloucestershire (U.K.) flooding that occurred in year 2007.

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Section: Electromagnetic Backgroundmentioning

confidence: 99%

Section: Conclusion and Future Extensions Of The Approachesmentioning

confidence: 99%

Flooding Water Depth Estimation With High-Resolution SAR

Iervolino

Guida

Iodice

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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“…SAR (Synthetic Aperture Radar) sensors, on the other hand, thanks to their allweather and day-and-night capabilities, guarantee a monitoring of the disaster despite of the atmospheric or light conditions, which is particularly vital in urban settlements where the damages are the most substantial. Normally, buildings in urban areas lead to a rising double-backscatter intensity in SAR images, due to the dihedral surface they form [3]. However, the change in the urban radar return caused by the flood is hard to accurately measure, due to its dependency on various parameters (e.g.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated estimation of the local flood depth on SAR images

Benoudjit

Guida

2017

2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI)

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In the context of a flooding, a clear cloud-free SAR (Synthetic Aperture Radar) image proves mainly useful to retrieve flood features that can provide an extensive understanding of the disaster. Among these features, extremely important is the water depth on which this paper will focus by looking for a semi-automated algorithm for its estimation in the neighborhood of a given building from a pair of SAR images.In this study, two SAR images acquired during dry and flooded conditions are necessary, as well as a DSM (Digital Surface Model) to give an a priori knowledge of the height of the building and its footprint. The whole process is divided into two main parts: First, an extraction of the building's double-bounce contribution using Genetic Algorithms, then the computation of the inundated building's height, to eventually evaluate the water level locally in the neighborhood of this building.Thanks to the semi-automation of the double-reflection line retrieval, the execution time of the whole process was reduced from a few minutes (time to manually delineate the double-bounce line) to a few seconds, while keeping an error in the estimated flood depth in the order of a few decimeters (35cm on average).

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“…The look angle is 24°, the spatial resolution respectively 3.3m and 1.2m for the azimuth and the slant range, the polarization mode is HH. This polarization provides a better discrimination between flooded and non-flooded areas [5]. First of all, a threshold segmentation has been applied to the data set.…”

Section: Case Study and Data Setmentioning

confidence: 99%

A new local approach for flooding level estimation in urban areas using single SAR images

Iervolino¹,

Diessa²,

Iodice³

et al. 2011

2011 Joint Urban Remote Sensing Event

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-The aim of this paper is to evaluate the level of flooding in proximity of sensible targets in urban areas using only one Synthetic Aperture Radar (SAR) image. To this purpose a two-step algorithm is here proposed: first the flooded areas are detected in the SAR image; and then the water level is retrieved by inverting scattering models developed for urban areas and now properly adapted for the case at issue. The retrieval is performed through a local approach where the a-priori knowledge of the target ground truth and two gauges in the premises is required. The approach is tested on a High Resolution (HR) TerraSAR-X image acquired during the flooding occurred in the Gloucestershire in July 2007.

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GB-SAR/PiSAR simultaneous experiment for a trial of flood area detection

Cited by 4 publications

References 0 publications

Flooding Water Depth Estimation With High-Resolution SAR

Flooding Water Depth Estimation With High-Resolution SAR

Semi-automated estimation of the local flood depth on SAR images

A new local approach for flooding level estimation in urban areas using single SAR images

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