A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case

Chini, Marco; Hostache, Renaud; Giustarini, Laura; Matgen, Patrick

doi:10.1109/tgrs.2017.2737664

Cited by 228 publications

(204 citation statements)

References 56 publications

Supporting

Mentioning

170

Contrasting

Unclassified

Order By: Relevance

“…The vast majority of local automatic thresholding approaches have been developed for utilizing radar images [22,[26][27][28][29], while the proposed approach uses as input optical satellite images to detect water extent. The aim of this paper is to introduce an unsupervised approach capable of generating inundation maps relying on preprocessed S2 data without the need for simultaneous ground truth data.…”

Section: Discussionmentioning

confidence: 99%

“…The capability of a global thresholding algorithm to detect an optimal histogram threshold may be unsatisfying when the class proportions within the image are imbalanced [29]. Additionally, the water and non-water class distributions may be quite different when considering the complete image compared to image subsets [27], and this may hamper the ability to detect an optimal global threshold for the complete area.…”

mentioning

confidence: 99%

“…Several of the methods rely on image subsets of fixed size [24,[26][27][28] to estimate local thresholds, while there is a limited number of recent approaches that adapt the size of the subregions to the information content retrieved from the image [22,29]. The approach in Chini et al [29] estimates subregions of variable size, which allow the parameterization of the distribution of water and non-water classes, while the approach in Nakmuenwai et al [22] selects subregions of elliptical shape having a water and non-water cover ratio of nearly 1:1.…”

mentioning

confidence: 99%

“…The proposed approach efficiently exploits optical data, e.g., Sentinel-2 bands and extracted indices, while the vast majority of local automatic thresholding approaches are developed for utilizing radar data [22,24,[26][27][28][29]. Moreover, it estimates local thresholds relying on patches of expanding size, contrary to most of the approaches that use image subsets of a fixed size [24,[26][27][28].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

View full text Add to dashboard Cite

Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Doñana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Its subscript indicates VV or VH polarization. Directly, bitemporal SAR information with single polarization, denoted as VV or VH, is often used in the related studies (Chini et al, ; Giustarini et al, ; Kang et al, ). In this study, for the input information design, based on the radar RS physics, we have two considerations: (1) the VV and VH polarization information should be fused, for they can compensate each other; and (2)

σ_{VV}^{post} - σ_{VV}^{pre}

and

σ_{VH}^{post} - σ_{VH}^{pre}

; that is, the difference images for the VV and VH polarizations, respectively, should be added.…”

Section: Methodsmentioning

confidence: 99%

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Liu

Zheng

2019

JGR Oceans

View full text Add to dashboard Cite

This study develops an effective and robust method to mine bitemporal and dual-polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored deep convolutional neural network-based SAR coastal flooding mapping network (SARCFMNet) leverages two modifications to improve the accuracy and robustness: the physics-aware input information design and the regularization. The proposed SARCFMNet is applied to the mapping and impact analysis of the coastal inundation caused by 2017 Hurricane Harvey near Houston, Texas, USA. Six pairs of Sentinel-1 SAR images are analyzed along with corresponding ground truth data from Copernicus Emergency Management Service Rapid Mapping products and land-cover types from Google Earth and OpenStreetMap. Flooded areas of 4,000 km 2 are extracted and analyzed. In an analyzed scene, 76% of the flooded area was agriculture area like pasture and cultivated crops field. A flooding map series shows the inundation shrinking rate is about 1% of the analyzed scene per day after the passage of Harvey. However, there was a delayed inundation in Glen Flora, Texas, after the heavy raining period. The average mapping accuracy and F1 score, that is, the harmonic mean of recall and precision, are 0.98 and 0.88, respectively. The impact of wind and cost-sensitive loss functions on the development of SARCFMNet is also discussed. This study demonstrates the proposed method can accurately map hurricane-induced inundation. The method can also be readily extended to other multitemporal SAR imagery classification applications. Plain Language SummaryCoastal flooding caused by hurricanes has a huge impact on the safety and properties of people in coastal areas. Coastal flooding mapping using synthetic aperture radar (SAR) data is a low-cost and wide-coverage means; moreover, SAR has day-and-night, all-weather observation abilities. High-accuracy and robust coastal flooding mapping from SAR imagery information will help the management to formulate more targeted responses and the researchers to better understand coastal flooding mechanisms and develop more precise forecasting models. Based on deep convolutional neural networks, a specially tailored SAR coastal flooding mapping network method is developed to mine bitemporal and dual-polarization SAR imagery information for coastal flooding mapping. The performance of the proposed SAR coastal flooding mapping network is demonstrated by mapping the coastal flooding near Houston, Texas, USA, after 2017 Hurricane Harvey. The study verifies that the mapping accuracy and robustness of the proposed method are better than those of the classic and widely applied deep convolutional neural network method and also shows the mapping products contribute to a better understanding of the geospatial and temporal characteristics of coastal flooding.

show abstract

Flood Mapping Using Synthetic Aperture Radar Sensors From Local to Global Scales

Dasgupta

Grimaldi

Ramsankaran

et al. 2018

Geophysical Monograph Series

View full text Add to dashboard Cite

A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case

Cited by 228 publications

References 56 publications

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Coastal Inundation Mapping From Bitemporal and Dual‐Polarization SAR Imagery Based on Deep Convolutional Neural Networks

Flood Mapping Using Synthetic Aperture Radar Sensors From Local to Global Scales

Contact Info

Product

Resources

About