OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion
نویسندگان
چکیده
Regions around the world experience adverse climate-change-induced conditions that pose severe risks to normal and sustainable operations of modern societies. Extreme weather events, such as floods, rising sea levels, storms, stand characteristic examples impair core services global ecosystem. Especially floods have a impact on human activities, hence, early accurate delineation disaster is top priority since it provides environmental, economic, societal benefits eases relief efforts. In this article, we introduce OmbriaNet, deep neural network architecture, based convolutional networks, detects changes between permanent flooded water areas by exploiting temporal differences among flood events extracted different sensors. To demonstrate potential proposed approach, generated OMBRIA, bitemporal multimodal satellite imagery dataset for image segmentation through supervised binary classification. It consists total number 3.376 images, synthetic aperture radar from Sentinel-1, multispectral Sentinel-2, accompanied with ground-truth images produced data derived experts provided Emergency Management Service European Space Agency Copernicus Program. The covers 23 globe, 2017 2021. We collected, co-registrated preprocessed in Google Earth Engine. validate performance our method, performed benchmarking experiments OMBRIA compared several competitive state-of-the-art techniques. experimental analysis demonstrated formulation able produce high-quality maps, achieving superior over state-of-the-art. provide dataset, well OmbriaNet code at: https://github.com/geodrak/OMBRIA .
منابع مشابه
Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique
Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...
متن کاملDeep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1
Mapping winter vegetation quality coverage is a challenge problem of remote sensing. This is due to the cloud coverage in winter period, leading to use radar rather than optical images. The objective of this paper is to provide a better understanding of the capabilities of radar Sentinel-1 and deep learning concerning about mapping winter vegetation quality coverage. The analysis presented in t...
متن کاملComprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be processed and calibrated manually as done in the past. Here, we describe a methodology to process the S...
متن کاملDeformation monitoring using Sentinel-1 SAR data
This paper describes the data processing and analysis procedure implemented by the authors to analyse Sentinel-1 data. The procedure is an advanced Differential Interferometric SAR (DInSAR) technique that generates deformation maps and time series of deformation from multiple SAR images acquired over the same site. The second part of the paper illustrates the results of the procedure. The first...
متن کاملDetermination of flood-prone areas using Sentinel-1 Radar images (Case study: Flood on March 2019, Kashkan River, Lorestan Province)
Determination of flood-prone areas using Sentinel-1 Radar images (Case study: Flood on March 2019, Kashkan River, Lorestan Province) Introduction Although natural hazards occur in all parts of the world, their incidence is higher in Asia than in any other part of the world. Natural phenomena are considered as natural hazards when they cause damage or financial losses to human beings. Iran ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2022
ISSN: ['2151-1535', '1939-1404']
DOI: https://doi.org/10.1109/jstars.2022.3155559