无监督域调整用于遥感语义细分与二维离散波纹变换的2D离散波纹变换.
Junying Zeng1, Yajin Gu2, Chuanbo Qin3
1School of Electronics and Information Engineering, Wuyi University, Guangdong, 529020, China.
Scientific reports
|October 9, 2024
概括
本研究引入了一种新的双空间生成对抗域适应细分框架 (DS-DWTGAN),以改善远程传感图像的跨域细分. 该方法增强了模型的稳定性和适应性,性能优于最先进的方法.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 遥感中的跨领域细分受到光谱,尺度和分辨率的变化所挑战.
- 现有的域调整方法往往优先考虑风格而不是语义信息,或忽视远程传感的具体情况,导致不稳定的训练和有偏见的结果.
研究的目的:
- 提出一种新的双空间生成对抗域适应细分框架,DS-DWTGAN,以最大限度地减少域差异.
- 为了提高语义细分模型在不同遥感领域的性能和稳定性.
主要方法:
- 引入了一个双空间生成对抗网络,结合波形变换分支来捕获频率和语义信息.
- 综合输出调整和数据增强策略,用于域不变特征获取.
- 杆波段转换以保留频域中的语义细节,减轻转换偏差.
主要成果:
- 在PotsdamIRRG到Vaihingen任务中取得了卓越的表现,mIoU为56.04%和mF1为67.28%.
- 性能比最先进的方法优于2.81% (mIoU) 和2.08% (mF1).
- 在域迁移过程中证明了增强模型稳定性和减少噪声干扰.
结论:
- 拟议的DS-DWTGAN框架有效地解决了用于遥感图像的跨域语义细分方面的挑战.
- 波形变换集成和适应策略显著提高了模型的适应性和性能.
- 与现有方法相比,该方法在无人机遥感图像的无监督语义细分方面表现出更高的效率.
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