优化损失和自我注意力,以增强远程传感图像分类领域的适应性
Pranav Kumar1, Jimson Mathew2, Rakesh Kumar Sanodiya3
1Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India. 1821cs09@iitp.ac.in.
Scientific reports
|November 17, 2025
概括
本研究引入了一种用于遥感 (RS) 图像分类的新领域适应框架,将多次损失的注意力机制集成在一起,以提高未标记数据的准确性. 该方法有效地解决了由不同的条件引起的域转移,提高了RS系统的稳定性.
科学领域:
- 计算机科学 计算机科学
- 地理空间科学 地理空间科学
- 人工智能的人工智能
背景情况:
- 远程传感 (RS) 图像分类传统上需要广泛的标记数据,这是昂贵的和劳动密集型的获取.
- 现有的方法难以处理大规模,高维的数据,并且由于采集条件或传感器的变化而导致的领域转移.
- 域名适应技术旨在将知识从标记到未标记的域名转移,但现有的方法有局限性.
研究的目的:
- 开发一个统一的域适应框架,用于遥感图像分类.
- 将初级,二级和损失与自我注意力机制相结合.
- 在各种最先进的神经网络模型和数据集中评估拟议的方法的有效性.
主要方法:
- 提出了一个新的框架,包括初级 (中心,三重) 和二次 (MMD, CORAL,) 损失.
- 在统一的框架内集成了一个自我注意机制.
- 在使用RSSCN7,NWPU-RESISC45,AID和UCMerced数据集的神经网络模型 (VGG,ResNet,AlexNet,GoogLeNet,EfficientNet,MobileNet,ViT) 上评估了性能.
主要成果:
- 综合框架在处理远程传感图像分类领域转移方面表现出有效性.
- 系统审查证实了最新的神经网络上各种损失的表现.
- 实验验证实了拟议的方法,使用了分类和前最后一层的特征.
结论:
- 拟议的注意力集成领域适应框架为遥感图像分类提供了更强大,更准确的解决方案.
- 这种方法有效地解决了分布变化和对未标记数据的需求所带来的挑战.
- 这些发现为改进的遥感系统铺平了道路,能够适应多样化和不断变化的环境.
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