压缩学习用于合成孔径雷达数据的分类和重建
Mobina Keymasi1, Omid Ghozatlou1, Miguel Heredia Conde2
1Research Center for Spatial Information (CEOSpaceTech), The National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
合成光圈雷达 (SAR) 的压缩学习 (CL) 减少了图像处理的数据量. 联合训练压缩层显著提高了SAR数据的分类准确性和重建质量.
科学领域:
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 合成孔径雷达 (SAR) 产生了大量数据集,对高效的处理和分析提出了挑战.
- 压缩学习 (CL) 提供了一种方法来减少数据量,同时保留SAR应用程序的基本信息.
研究的目的:
- 为SAR数据处理引入一种新的压缩学习框架.
- 调查三个场景:直接分类,图像重建和联合分类-重建.
- 开发一个可训练的压缩层,用于适应性,特定任务的数据表示.
主要方法:
- 一个网络架构,用于压缩的线性转换层和用于分类和重建的多层感知子 (MLPs).
- 实施三个不同的CL场景:直接分类,重建和联合分类-重建.
- 对联合场景进行端到端的训练,以优化压缩层以满足特定任务.
主要成果:
- 联合分类和重建场景表明,与固定压缩方法相比,性能优越.
- 观察到分类准确度和图像重建质量的显著改善.
- 可训练式压缩层的自适应性增强了推断和数据恢复.
结论:
- 适应性压缩学习,特别是通过联合培训,为高效的SAR数据处理提供了一个有希望的方法.
- 拟议的框架有效地平衡了数据减少与高分类和重建性能.
- 这项工作突出了可训练压缩层在增强SAR图像分析和数据管理方面的潜力.
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