Sentinel-1-based water and flood mapping: benchmarking convolutional neural networks against an operational rule-based processing chain

In this study, the effectiveness of several convolutional neural network architectures (AlbuNet-34/FCN/DeepLabV3+/U-Net/U-Net++) for water and flood mapping using Sentinel-1 amplitude data is compared to an operational rule-based processor (S-1FS). This comparison is made using a globally distributed dataset of Sentinel-1 scenes and the corresponding ground truth water masks derived from Sentinel-2 data to evaluate the performance of the classifiers on a global scale in various environmental conditions. The impact of using single versus dual-polarized input data on the segmentation capabilities of AlbuNet-34 is evaluated. The weighted cross entropy loss is combined with the Lovász loss and various data augmentation methods are investigated. Furthermore, the concept of atrous spatial pyramid pooling used in DeepLabV3+ and the multiscale feature fusion inherent in U-Net++ are assessed. Finally, the generalization capacity of AlbuNet-34 is tested in a realistic flood mapping scenario by using additional data from two flood events and the Sen1Floods11 dataset. The model trained using dual polarized data outperforms the S-1FS significantly and increases the intersection over union (IoU) score by 5%. Using a weighted combination of the cross entropy and the Lovász loss increases the IoU score by another 2%. Geometric data augmentation degrades the performance while radiometric data augmentation leads to better testing results. FCN/DeepLabV3+/U-Net/U-Net++ perform not significantly different to AlbuNet-34. Models trained on data showing no distinct inundation perform very well in mapping the water extent during two flood events, reaching IoU scores of 0.96 and 0.94, respectively, and perform comparatively well on the Sen1Floods11 dataset

Water Mapping Using Synthetic Aperture Radar Data and Convolutional Neural Networks

Water mapping to derive flood parameters using Synthetic Aperture Radar data is an estab-lished procedure in emergency situations. In this study, the effectiveness of convolutionalneural networks (AlbuNet-34) for the purpose of water mapping using Sentinel-1 data is in-vestigated and compared to the performance of a state-of-the-art rule-based processor forwater mapping. This comparison is made using a reference dataset containing 67 globallydistributed Sentinel-1 scenes and the corresponding ground truth water masks derived fromSentinel-2 data to evaluate the performance of the classifiers on a global scale in variousenvironmental conditions. Various semi-random undersampling strategies for balancing thedataset are explored and the effect of the sample size on the performance of the models is in-vestigated. The cross entropy loss is compared to the region-based Lovász loss function andvarious data augmentation methods (flip, zoom, intensity variation, rotation, speckle simula-tion) are assessed. Furthermore, the impact of using single polarized VV or VH data and dualpolarized VV-VH data on the segmentation capabilities of AlbuNet-34 is evaluated. Finally,the concept of atrous spatial pyramid pooling used in a DeepLabV3+ model with a ResNet-50 encoder is assessed with respect to segmentation performance. The IoU scores on theglobal test set of 14 Sentinel-1 scenes vary by 0.11, depending on the sampling strategy, andthe Lovász loss increases the test IoU score by 0.01 compared to the cross entropy loss.Left-right flip and intensity augmentation improve the performance of the model, zooming androtation show only minor impact and speckle simulation decreases the performance. Themodel trained using VV-VH polarized data outperforms the rule-based flood processor andincreases accuracy by 0.01, recall by 0.03, precision by 0.04, F1 by 0.06, Kappa by 0.06 and IoU by 0.06. DeepLabV3+ yields results comparable to AlbuNet-34

Laboruntersuchungen heterogener Reaktionen auf stratosphaerisch relevanten Oberflaechen Abschlussbericht

Experimental work has been carried out to investigate the role of heterogeneous processes involving bromine and NO_x containing species in the chemistry of the lower stratosphere. Two experimental set-ups were constructed during the course of the project and a number of physical and chemical parameters of importance for reactions on sulphuric acid and ice surfaces were obtained. We measured solubilities of HCl, HBr, HOBr and NO_3 in cold, concentrated sulphuric acid, and were able to quantify the products and define the mechanism of the important aqueous-phase reaction between HOBr and HCl in H_2SO_4. The reaction of HNO_3 on water-ice surfaces was also investigated. (orig.)SIGLEAvailable from TIB Hannover: DtF QN1(83,46) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung (BMBF), Bonn (Germany)DEGerman