Automatic Flood Monitoring based on SAR Intensity and Interferometric Coherence using Machine Learning

2020 ◽  
Author(s):  
Binayak Ghosh ◽  
Mahdi Motagh ◽  
Mahmud Haghshenas Haghighi ◽  
Setareh Maghsudi
Keyword(s):  
Machine Learning ◽  
Open Water ◽  
Semantic Segmentation ◽  
Flood Mapping ◽  
Assistance Programs ◽  
Amplitude Data ◽  
Flood Monitoring ◽  
Sar Data ◽  
Flood Detection ◽  
Flood Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. Flood mapping algorithms are usually based on automatic thresholding algorithms for the initialization of the classification process in SAR amplitude data. These thresholding processes like Otsu thresholding, histogram leveling etc., are followed by clustering techniques like K-means, ISODATA for segmentation of water and non-water areas. These methods are capable of extracting the flood extent if there is a significant contrast between water and non-water areas in the SAR data. However, the classification result may be related to overestimations if non-water areas have a similar low backscatter as open water surfaces and also, these backscatter values differentiate from VV and VH polarizations. Our method aims at improving existing satellite-based emergency mapping methods by incorporating systematically acquired Sentinel-1A/B SAR data at high spatial (20m) and temporal (3-5 days) resolution. Our method involves a supervised learning method for flood detection by leveraging SAR intensity and interferometric coherence as well as polarimetry information. </span></span><span xml:lang="EN-US" data-contrast="auto"><span>It uses multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. By incorporating multitemporal satellite imagery, our method allows for rapid and accurate post-disaster damage assessment and can be used for better coordination of medium- and long-term financial assistance programs for affected areas. In this paper, we present a strategy using machine learning for semantic segmentation of the flood map, which extracts the </span></span><span xml:lang="EN-US" data-contrast="auto"><span>spatio</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-temporal information from the SAR images having both </span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensity</span></span><span xml:lang="EN-US" data-contrast="auto"><span> as well coherence bands. The flood maps produced by the fusion of intensity and coherence are validated against state-of-the art methods for producing flood maps.</span></span><span> </span></p>

scholarly journals Potentiality and Limitations of Open Satellite Data for Flood Mapping

2018 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Calò ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Satellite Data ◽  
Low Cost ◽  
Ground Truth ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Ground Truth Data ◽  
Flood Peak ◽  
Sar Data ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

scholarly journals Flood Mapping Using InSAR Coherence Map

2014 ◽  
Vol XL-7 ◽  
pp. 161-164 ◽  
Author(s):  
S. Selmi ◽  
W. Ben Abdallah ◽  
R. Abdelfatteh
Keyword(s):  
Static Analysis ◽  
Decision Rule ◽  
Flood Mapping ◽  
Sar Image ◽  
Optical Images ◽  
Ratio Approach ◽  
Sar Data ◽  
Flood Detection ◽  
New Formulation ◽  
Flooded Areas

Classic approaches for the detection of flooded areas are based on a static analysis of optical images and/or SAR data during and after the event. In this paper, we aim to extract the flooded zones by using the SAR image coupled with the InSAR coherence. A new formulation of the ratio approach for flood detection is given considering InSAR coherence. Our contribution is to take advantage from the coherence map provided using the InSAR pairs (one before and one after the event) to enhance the detection of flooded areas. We explore the fact that the coherence values during and after the flood are mainly differents on the flooded zones and we give a more suitable flood decision rule using this assumption. The proposed approach is tested and validated in the case of the flood taken place in 2005 in the region of Kef in Tunisia.

scholarly journals Flood Detection/Monitoring Using Adjustable Histogram Equalization Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fakhera Nazir ◽  
Muhammad Mohsin Riaz ◽  
Abdul Ghafoor ◽  
Fahim Arif
Keyword(s):  
State Of The Art ◽  
User Interaction ◽  
Histogram Equalization ◽  
Monitoring Technique ◽  
Output Quality ◽  
Flood Monitoring ◽  
Equalization Technique ◽  
Flood Detection ◽  
Processing Steps ◽  
Flood Maps

Flood monitoring technique using adjustable histogram equalization is proposed. The technique overcomes the limitations (overenhancement, artifacts, and unnatural look) of existing technique by adjusting the contrast of images. The proposed technique takes pre- and postimages and applies different processing steps for generating flood map without user interaction. The resultant flood maps can be used for flood monitoring and detection. Simulation results show that the proposed technique provides better output quality compared to the state of the art existing technique.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Understanding Rooftop PV Panel Semantic Segmentation of Satellite and Aerial Images for Better Using Machine Learning

2021 ◽  
pp. 100057
Author(s):  
Peiran Li ◽  
Haoran Zhang ◽  
Zhiling Guo ◽  
Suxing Lyu ◽  
Jinyu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Pv Panel

scholarly journals A Tool for Pre-Operational Daily Mapping of Floods and Per-Manent Water Using Sentinel-1 Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1342
Author(s):  
Luca Pulvirenti ◽  
Giuseppe Squicciarino ◽  
Elisabetta Fiori ◽  
Luca Ferraris ◽  
Silvia Puca
Keyword(s):  
Region Growing ◽  
Open Water ◽  
Optical Data ◽  
Management Service ◽  
Data Generation ◽  
End User ◽  
Mapping Algorithm ◽  
Output Layer ◽  
Water Mapping ◽  
Flood Maps

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed.

scholarly journals Crop Classification by Machine Learning Algorithm Using C-band SAR Data

2017 ◽  
Vol 56 (4) ◽  
pp. 143-148
Author(s):  
Yuki YAMAYA ◽  
Hiroshi TANI ◽  
Xiufeng WANG ◽  
Rei SONOBE ◽  
Nobuyuki KOBAYASHI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Crop Classification ◽  
Sar Data

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

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