Monitoring and Mapping Floods and Floodable Areas in the Mekong Delta (Vietnam) Using Time-Series Sentinel-1 Images, Convolutional Neural Network, Multi-Layer Perceptron, and Random Forest
نویسندگان
چکیده
The annual flood cycle of the Mekong Basin in Vietnam plays an important role hydrological balance its delta. In this study, we explore potential C-band Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring flooded flood-prone areas An Giang province Delta, especially rice fields. Time floodable area maps were generated from five images per month taken during wet season (6–7 months) over two years (2019 2020). methodology was based on automatic image classification through application Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) random forests (RFs). Based segmentation technique, a three-level algorithm developed to generate development floods season. A modification backscatter intensity noted both polarizations, accordance with evolution phenology results show that CNN-based methods can produce more reliable (99%) compared MLP RF (97%). Indeed, process, feature extraction CNNs has demonstrated effective improvement prediction performance land use cover (LULC) classes, deriving complex decision boundaries between non-flooded areas. 53% 58% paddies 9% 14% built-up are affected by flooding 2019 2020 respectively. Our could support database hazard management Delta.
منابع مشابه
scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network
today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...
A multi-scale convolutional neural network for automatic cloud and cloud shadow detection from Gaofen-1 images
The reconstruction of the information contaminated by cloud and cloud shadow is an important step in pre-processing of high-resolution satellite images. The cloud and cloud shadow automatic segmentation could be the first step in the process of reconstructing the information contaminated by cloud and cloud shadow. This stage is a remarkable challenge due to the relatively inefficient performanc...
متن کاملAn Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network
Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image arti...
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: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15082001