频谱带选择和ANIMR-GAN用于高性能多频谱煤炭带分类
Qingya Wang1,2,3, Huaitian Hua4, Liangliang Tao5
1College of Information Engineering, Jiujiang Vocational and Technical College, Jiujiang, 332000, Jiangxi, People's Republic of China. qqwqy@ecut.edu.cn.
Scientific reports
|April 2, 2024
概括
本研究引入了一种先进的深度学习模型,用于增强煤炭格分类中的多光谱成像 (MSI). 改进的分辨率和识别性能提高了矿物加工的效率和可持续性.
科学领域:
- 地质科学和矿业工程 矿业科学和矿业工程
- 计算机视觉和机器学习
- 环境科学与技术 环境科学与技术
背景情况:
- 有效的煤分类对于环境保护和资源回收至关重要.
- 地下多光谱成像 (MSI) 面临着低分辨率和糟糕的识别挑战.
- 开发先进的计算方法是克服这些局限性的必要.
研究的目的:
- 为了提高地下MSI设备的分辨率和识别性能,用于煤排序.
- 为超分辨率重建提出和验证一种新的深度学习模型.
- 为了确定最佳的光谱频段,以准确地对煤炭和带进行分类.
主要方法:
- 开发一个基于注意力的多层次残留网络 (ANIMR),集成到CycleGAN启发的超分辨率模型 (ANIMR-GAN) 中.
- 在ANIMR-GAN架构中整合了区分器和损失功能的改进.
- 培训和验证使用600个和120个煤炭和河MSI样本,然后使用随机森林算法进行分类.
主要成果:
- 当与随机森林分类器相结合时,ANIMR-GAN模型实现了最高分类准确率为97.78%,平均准确率为93.72%.
- 959.37纳米的光谱带被认为是区分煤炭与的最佳范围.
- 与现有的超分辨率技术相比,ANIMR-GAN表现出更高的性能.
结论:
- 拟议的ANIMR-GAN有效地解决了地下MSI的低分辨率和糟糕的识别问题.
- 这项技术促进了智能和高效的分类,为可持续的矿物加工做出了贡献.
- 这些发现为采矿行业更广泛采用先进的成像和人工智能铺平了道路.
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