Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Kang, Wenchao; Xiang, Yuming; Wang, Feng; Wan, Li‐Jun; You, Hongjian

doi:10.3390/s18092915

Cited by 58 publications

(47 citation statements)

References 28 publications

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“…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%

“…It is essential to merge postevent very high resolution optical images into the framework, but this data source is difficult to obtain in disasters like hurricanes. Kang et al () verified that the DCNN‐based flooding detection method is more accurate than the aforementioned Type‐1 and ‐2 methods for SAR images, but this study treated each temporal and each polarization SAR image separately.…”

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

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

Liu

Zheng

2019

JGR Oceans

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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.

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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%

Section: Introductionmentioning

confidence: 96%

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

Liu

Zheng

2019

JGR Oceans

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show abstract

Section: Datamentioning

confidence: 99%

“…Launched, as an ocean surveillance satellite, from the Taiyuan space center on 10 August 2016, the Chinese Gaofen-3 satellite, equipped with a multi-polarized C-band SAR at meter-level resolution, can operate in twelve different working modes [41,42]. With a design life of eight years [43], Gaofen-3 has been in operation officially since January 2017 [42]. The Gaofen-3 SAR image, which meets the accuracy for ship detection [44,45], is competent in numerous applications, such as in monitoring the global ocean and land resources [46].…”

Section: Datamentioning

confidence: 99%

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

et al. 2019

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Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F1 score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization.

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“…Also, stacked modules of CNN and one Recurrent Neural Network (RNN) were utilized to segment flooded areas (Rahnemoonfar et al, 2018). Kang et al, (2018) embedded eight convolutional, two deconvolutional, and fusing layers in their proposed CNN. They integrated deep (global) and shallow (local) features via fusion layers to map flooded areas using GaoFen-3 SAR images.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network for Flood Mapping Using Sentinel-1 and SRTM Dem Data: Case Study in Poldokhtar-Iran

Hosseiny

Ghasemian

Amini

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Flood contributes a key role in devastating natural and man-made areas. Floods usually are occurred when there is a considerable number of clouds in the sky making optic data useless. Synthetic aperture radar (SAR) images can be a valuable data source in earth observation tasks. The most important characteristic of the radar image is its ability to penetrate the cloud and dust. Therefore, monitoring earth in cloudy or rainy weather can be available by this kind of dataset. In the last few years by improving machine learning methods and development of convolutional neural networks in remote sensing applications we are facing with extremely high improvement in classification tasks. In this paper, we use dual-polarized VV and VH backscatter values of Sentinel-1 and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) dataset in a proposed convolutional neural network to generate a land cover map of a flooded area before and after happening. Obtained classification results vary between 93.3% to 98.5% for different training sizes. By comparing the generated classified maps, flooded areas of each class can be extracted.

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Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Cited by 58 publications

References 28 publications

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

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

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

A Convolutional Neural Network for Flood Mapping Using Sentinel-1 and SRTM Dem Data: Case Study in Poldokhtar-Iran

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