Examples of DAFNE application to multi-temporal and multi-frequency remote sensed images and geomorphic data for accurate flood mapping.

2020 ◽  
Author(s):  
Annarita D'Addabbo ◽  
Alberto Refice ◽  
Francesco Lovergine ◽  
Guido Pasquariello
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Bayesian Network ◽  
Optical Data ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Multi Temporal ◽  
Intensity Images ◽  
Ancillary Information ◽  
Flood Maps

<p>DAFNE(Data Fusion by Bayesian Network) is a Matlab-based open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach [1]. It is based on Bayesian Networks and it is composed of five modules, which can be easily modified or upgraded to meet different user needs. DAFNE provides, as output products, probabilistic flood maps, i.e., for each pixel in a given output map, the probability value that the corresponding area has been reached from the inundation is reported. Moreover, if remote sensed images have been acquired in different days during a flood event, DAFNE allows to follow the inundation temporal evolution.</p><p>It is well known that flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground [2]. In particular, the combined analysis of multi-temporal and multi-frequency SAR intensity and coherence trends, together with optical data and other ancillary information, can be particularly useful to map flooded area, characterized by different land cover and land use [3]. Here a recent upgrade is presented that allows to consider as input data multi-frequency SAR intensity images, such as X-band, C-band and L-band images.</p><p>Three different inundation events have been considered as applicative examples: for each one, multi-temporal probabilistic flood maps have been produced by combining multi-temporal and multi-frequency SAR intensity images images (such as COSMO-SkyMed , Sentinel-1 images and ALOS 2 images), InSAR coherence and optical data (such as Landsat 5 images or High Resolution images), together with geomorphic and other ground information. Experimental results show good capabilities of producing accurate flood maps with computational times compatible with a near real time application.</p><p> </p><p>[1] A. D’Addabbo, A. Refice, F. Lovergine, G. Pasquariello, DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping. Computer and Geoscience 112 (2018), 64-75.</p><p>[2] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711–2722, 2014.</p><p>[3] A. D’Addabbo et al., “A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data,” IEEE Transactions on Geoscience and Remote Sensing, vol.54, n.6, pp.3612-3625, 2016.</p><p> </p>

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

2018 ◽  
Vol 10 (11) ◽  
pp. 1673 ◽  
Author(s):  
Davide Notti ◽  
Daniele Giordan ◽  
Fabiana Caló ◽  
Antonio Pepe ◽  
Francesco Zucca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Flood Hazard ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Po River ◽  
Ground Truth Data ◽  
Hazard Management ◽  
Flooded Areas ◽  
Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of 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. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps.

scholarly journals DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

2018 ◽  
Vol 112 ◽  
pp. 64-75 ◽  
Author(s):  
Annarita D'Addabbo ◽  
Alberto Refice ◽  
Francesco P. Lovergine ◽  
Guido Pasquariello
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Flood Mapping ◽  
Ancillary Data ◽  
Matlab Toolbox

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

crops planting area identification and analysis based on multi-source high resolution remote sensing data

2020 ◽  
Author(s):  
Lei Wang ◽  
Haoran Sun ◽  
Wenjun Li ◽  
Liang Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Classification Accuracy ◽  
Kappa Coefficient ◽  
Optical Data ◽  
Accurate Estimation ◽  
Support Vector ◽  
Random Forest Method ◽  
Multi Temporal ◽  
Sar Data

<p>Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images,  efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.</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 THE SEN1-2 DATASET FOR DEEP LEARNING IN SAR-OPTICAL DATA FUSION

2018 ◽  
Vol IV-1 ◽  
pp. 141-146 ◽  
Author(s):  
M. Schmitt ◽  
L. H. Hughes ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Data Fusion ◽  
Training Data ◽  
Sensor Data ◽  
Optical Data ◽  
Multiple Sensors ◽  
Optical Images ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques

<p><strong>Abstract.</strong> While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing. In particular, this holds for applications involving data from multiple sensors with heterogeneous characteristics. One example for that is the fusion of synthetic aperture radar (SAR) data and optical imagery. With this paper, we publish the <i>SEN1-2</i> dataset to foster deep learning research in SAR-optical data fusion. <i>SEN1-2</i> comprises 282;384 pairs of corresponding image patches, collected from across the globe and throughout all meteorological seasons. Besides a detailed description of the dataset, we show exemplary results for several possible applications, such as SAR image colorization, SAR-optical image matching, and creation of artificial optical images from SAR input data. Since <i>SEN1-2</i> is the first large open dataset of this kind, we believe it will support further developments in the field of deep learning for remote sensing as well as multi-sensor data fusion.</p>

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