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CNN-based burned area mapping using radar and optical data

Published:2021-07 Issue: Volume:260 Page:112468
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Belenguer-Plomer Miguel A.,Tanase Mihai A.,Chuvieco Emilio,Bovolo Francesca

Funder

European Space Agency

Government of Spain Ministry of Science and Innovation

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference114 articles.

1. Learning Activation Functions to Improve Deep Neural Networks;Agostinelli,2014

2. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets;Anantrasirichai;Remote Sens. Environ.,2019

3. Forest fires and climate change: causes, consequences and management options;Aponte;Int. J. Wildland Fire,2016

4. Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning;Ban;Sci. Rep.,2020

5. Tuning the parameters of an artificial neural network using central composite design and genetic algorithm;Bashiri;Sci. Iran.,2011

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3. FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images;Ecological Indicators;2024-10

4. Burned area detection from a single satellite image using an adaptive thresholds algorithm;International Journal of Digital Earth;2024-07-11

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