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ARM-Net:用于高光谱图像压缩的三相集成网络

Qizhi Fang^1,2, Zixuan Wang², Jingang Wang²

¹Liaoning General Aviation Academy, Shenyang 110136, China.

Sensors (Basel, Switzerland)

|April 28, 2025

概括

这项研究介绍了ARM-Net,一种新的高光谱图像压缩框架. 通过自适应地选择波段和重建光谱细节,ARM-Net提高了压缩效率和精度.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
数据压缩数据压缩

背景情况:

目前的高光谱图像压缩方法面临着高的计算复杂性,因为有大量的光谱带.
现有的技术在资源有限的环境下与性能作斗争.

研究的目的:

开发一个高效和高保真度的高光谱图像压缩框架.
解决现有方法的计算挑战,同时保持光谱信息.

主要方法:

建议建立一个三相混合框架 (ARM-Net).
适应频段选择用于减少计算负载.
采用采样带集群的高保真压缩和用于损失补偿的重建网络.

主要成果:

在7个超频谱数据集上,ARM-Net表现出了与最先进的方法相比的显著改进.
实现了1-2 dB更高的峰值信号噪声比率 (PSNR) 和多尺度结构相似性指数 (MS-SSIM) 测量.
将平均光谱角度映射器 (SAM) 减少了大约0.1.1.

结论:

拟议的ARM-Net框架有效地平衡了压缩效率和超光谱图像的重建质量.
在资源有限的场景中,ARM-Net为高光谱图像压缩提供了可行的解决方案.

关键词:

超光谱图像压缩方法经常性的光谱注意力机制.空间光谱注意力机制频谱重建的重建

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