Automated Extraction of Visible Floodwater in Dense Urban Areas from RGB Aerial Photos

Zhang, Ying; Crawford, Peter

doi:10.3390/rs12142198

Cited by 12 publications

(11 citation statements)

References 31 publications

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“…At this time, the need to know the extent of the flood phenomenon to reduce the risk generated became evident. These methods, which are based mainly on the processing of geospatial information without requiring a high level of experience of those who use it, create disparities in the analysis of the phenomenon with the risk of results and methods inconsistent with the field reality [123][124][125][126][127][128]. Numerous methodologies that are presented as a novelty are not found in the current practical activity, as there is no validation of them in real-case scenarios.…”

Section: Discussionmentioning

confidence: 99%

An Overview of Flood Risk Analysis Methods

Diaconu

Costache

Popa

2021

Water

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Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

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Section: Discussionmentioning

confidence: 99%

An Overview of Flood Risk Analysis Methods

Diaconu

Costache

Popa

2021

Water

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“…Light consists of three primary colors popularly known as RGB, that is, Red-Green-Blue. 33,34 Computer screens can display images having a combination of these three colors band. When we combine these color bands, the result is a colored image having three primary color and their combination.…”

Section: Overview Of Nasa Landsat Programmentioning

confidence: 99%

“…If we consider only a single band of the image, it will appear like a grayscale image. Light consists of three primary colors popularly known as RGB, that is, Red‐Green‐Blue 33,34 . Computer screens can display images having a combination of these three colors band.…”

Section: Overview Of Nasa Landsat Programmentioning

confidence: 99%

Conversion of Landsat 8 multispectral data through modified private content‐based image retrieval technique for secure transmission and privacy

Shakya

Ramola

Vidyarthi

2020

Engineering Reports

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In this research work, we have developed a new image encryption-based algorithm for Landsat 8 satellite images. Image statistical parameters related to first-order image statistics, that is, mean (μ), SD (σ), and variance (σ 2) are used to obtain the feature values for both original images and their encrypted versions. Multispectral satellite images generally contain a wide range of bands, which is 9 to 11 specifically for the Landsat 8 satellite. Hence, the information content of these images is richer than typical images. Through this algorithm, we aim to reduce the image content by randomly selecting only 10% of the total image pixels. These pixels are used to create new images, the so-called encrypted images. The visual presentation of the encrypted image resembles a noisy picture. First-order image statistics are calculated for both original images and their encrypted counterparts. These parameters show a similarity in their numerical values for both original and encrypted images. Thus, noisy pictures can be used for transmission in place of the original image when an outcome is decided through statistical image features. The visual similarity in the original images and encrypted images is obtained from the histogram signature plot for both original and encrypted images. The proposed algorithm can hence be used to convert image data for secure transmission and privacy.

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“…The imaging of radar data cannot provide an intuitive image experience as well. Besides, satellite synthetic aperture radar (SAR) imagery of some urban areas is difficult to interpret because of the off-nadir viewing configuration, for example, the confusion of floodwater with a specular reflection of smooth land surfaces [4]. Optical remote-sensing data are used broadly for regional monitoring and mapping.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

et al. 2021

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Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

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Automated Extraction of Visible Floodwater in Dense Urban Areas from RGB Aerial Photos

Cited by 12 publications

References 31 publications

An Overview of Flood Risk Analysis Methods

An Overview of Flood Risk Analysis Methods

Conversion of Landsat 8 multispectral data through modified private content‐based image retrieval technique for secure transmission and privacy

An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

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