Research Article
Classification of Deforestation Factors in 6G Satellite Forest Images
@INPROCEEDINGS{10.1007/978-3-031-36011-4_17, author={Yuhai Li and Yuxin Sun and Xianglong Meng and Liang Xi}, title={Classification of Deforestation Factors in 6G Satellite Forest Images}, proceedings={6GN for Future Wireless Networks. 5th EAI International Conference, 6GN 2022, Harbin, China, December 17-18, 2022, Proceedings, Part I}, proceedings_a={6GN}, year={2023}, month={7}, keywords={6G network deforestation factor classification deep learning}, doi={10.1007/978-3-031-36011-4_17} }- Yuhai Li
Yuxin Sun
Xianglong Meng
Liang Xi
Year: 2023
Classification of Deforestation Factors in 6G Satellite Forest Images
6GN
Springer
DOI: 10.1007/978-3-031-36011-4_17
Abstract
The terrestrial satellite network will play an essential role in 6G. Through the satellite system, people can obtain a lot of ground image information to process tasks and feedback. Forest resource is an essential resource. Determining the causes of deforestation is crucial for the development and implementation of forest protection plans. In this article, we propose a novel deep neural network model for distinguishing drivers of deforestation events from satellite forest images. To solve the problems of image rotation caused by satellite angle rotation and image blurring caused by extreme weather occlusion during satellite image acquisition, we add data enhancement and a self-supervised rotation loss to the model to improve the robustness and adaptability. We use deforestation maps generated from Landsat 8 satellite imagery as a dataset and demonstrate that our approach achieves better results than the baselines.
