Graph-based few-shot learning with transformed feature propagation and optimal class allocation

R Zhang, S Yang, Q Zhang, L Xu, Y He, F Zhang - Neurocomputing, 2022 - Elsevier
Graph neural network has shown impressive ability to capture relations among support
(labeled) and query (unlabeled) instances in a few-shot task. It is a feasible way that features …

U2D2Net: Unsupervised Unified Image Dehazing and Denoising Network for Single Hazy Image Enhancement

B Ding, R Zhang, L Xu, G Liu, S Yang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Hazy images captured under ill-posed scenarios with scattering medium (ie haze, fog, or
smoke) are contaminated in visibility. Inevitably, these images are further degraded by …

Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope

VS Martins, DP Roy, H Huang, L Boschetti… - Remote Sensing of …, 2022 - Elsevier
High spatial resolution commercial satellite data provide new opportunities for terrestrial
monitoring. The recent availability of near-daily 3 m observations provided by the …

[HTML][HTML] Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network

ST Seydi, M Hasanlou, J Chanussot - Ecological Indicators, 2022 - Elsevier
Accurate and timely mapping of wildfire burned areas is crucial for post-fire management,
planning, and next subsequent actions. The monitoring and mapping of the burned area by …

Detection of forest fire using deep convolutional neural networks with transfer learning approach

HC Reis, V Turk - Applied Soft Computing, 2023 - Elsevier
Forest fires caused by natural causes such as climate change, temperature increase,
lightning strikes, volcanic activity or human effects are among the world's most dangerous …

Burned area detection and mapping using time series Sentinel-2 multispectral images

P Liu, Y Liu, X Guo, W Zhao, H Wu, W Xu - Remote Sensing of Environment, 2023 - Elsevier
Accurate mapping of burned areas (BAs) is essential for post-disaster reconstruction, air
quality assessment, and estimation of fire emissions. Remote sensing has become the most …

Burned area detection using multi-sensor SAR, optical, and thermal data in Mediterranean pine forest

S Abdikan, C Bayik, A Sekertekin, F Bektas Balcik… - Forests, 2022 - mdpi.com
Burned area (BA) mapping of a forest after a fire is required for its management and the
determination of the impacts on ecosystems. Different remote sensing sensors and their …

[HTML][HTML] Transformers for mapping burned areas in Brazilian Pantanal and Amazon with PlanetScope imagery

DN Gonçalves, JM Junior, AC Carrilho… - International Journal of …, 2023 - Elsevier
Pantanal is the largest continuous wetland in the world, but its biodiversity is currently
endangered by catastrophic wildfires that occurred in the last three years. The information …

Wildfires in the Siberian Arctic

VI Kharuk, ML Dvinskaya, ST Im, AS Golyukov… - Fire, 2022 - mdpi.com
Wildfires are increasingly understood as an ecological driver within the entire Arctic biome.
Arctic soils naturally store large quantities of C, as peat has formed throughout the …

DSMNN-Net: A deep siamese morphological neural network model for burned area mapping using multispectral sentinel-2 and hyperspectral PRISMA images

ST Seydi, M Hasanlou, J Chanussot - Remote Sensing, 2021 - mdpi.com
Wildfires are one of the most destructive natural disasters that can affect our environment,
with significant effects also on wildlife. Recently, climate change and human activities have …