DisasterGAN: Generative Adversarial Networks for Remote Sensing Disaster Image Generation
Rapid progress on disaster detection and assessment has been achieved with the development of deep-learning techniques and the wide applications of remote sensing images. However, it is still a great challenge to train an accurate and robust disaster detection network due to the class imbalance of e...
Guardado en:
Autores principales: | Xue Rui, Yang Cao, Xin Yuan, Yu Kang, Weiguo Song |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/c0619a0b74ed40bdb748e1496cd3b225 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
por: Saman Motamed, et al.
Publicado: (2021) -
Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze
por: Bo Jiang, et al.
Publicado: (2021) -
Remote Sensing Imagery Segmentation: A Hybrid Approach
por: Shreya Pare, et al.
Publicado: (2021) -
Water extraction model of multispectral optical remote sensing image
por: DENG Kaiyuan, et al.
Publicado: (2021) -
Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network
por: Chuan Du, et al.
Publicado: (2021)