SEMANTIC SEGMENTATION OF BURNED AREAS IN SATELLITE IMAGES USING A U-NET-BASED CONVOLUTIONAL NEURAL NETWORK

11 citations as recorded by crossref.

1. BA_EnCaps: Dense Capsule Architecture for Thermal Scrutiny Q. Safder et al. 10.1109/TGRS.2022.3166352
2. Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area J. Florath & S. Keller 10.3390/rs14030657
3. Forest Burned Area Detection Using a Novel Spectral Index Based on Multi-Objective Optimization B. Wu et al. 10.3390/f13111787
4. Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach A. Cho et al. 10.1109/JSTARS.2022.3225070
5. A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France A. Cerbelaud et al. 10.1016/j.isprsjprs.2021.10.013
6. A Quadratic Morphological Deep Neural Network Fusing Radar and Optical Data for the Mapping of Burned Areas S. Seydi et al. 10.1109/JSTARS.2022.3175452
7. BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images W. Bo et al. 10.1109/TGRS.2022.3197647
8. Mapping Pluvial Flood-Induced Damages with Multi-Sensor Optical Remote Sensing: A Transferable Approach A. Cerbelaud et al. 10.3390/rs15092361
9. U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model L. Yin et al. 10.3390/land12081602
10. Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network S. Seydi et al. 10.1016/j.ecolind.2022.108999
11. Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction R. Ghali & M. Akhloufi 10.3390/fire6050192