边缘意识和光谱空间特征学习的联合网络,用于高光谱图像分类
Jianfeng Zheng1, Yu Sun2, Yuqi Hao1
1College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了一个边缘感知网络,用于高光谱图像分类,改善特征提取. 拟议的方法通过适应性强化边缘特征和光谱重量来提高准确性和干扰免疫力.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 超光谱图像 (HSI) 分类对于HSI应用至关重要.
- 从HSI中丰富的光谱信息中提取深度表示特征是具有挑战性的.
- 现有的方法在增强边缘数据时经常引入噪声,并且未能强调光谱的重要性.
研究的目的:
- 提出一个边缘意识和光谱空间特征学习网络 (ESSN),以改进HSI分类.
- 为了应对深度特征提取,边缘表示和光谱重要性加权方面的挑战.
- 为了提高HSI分类中对干扰的准确性和稳定性.
主要方法:
- 开发了一个边缘意识和光谱空间特征学习网络 (ESSN).
- 整合了一个边缘特征增强块,以适应性地加强跨光谱带的边缘特征.
- 实现了带有自适应光谱重量调整的光谱空间特征提取块.
主要成果:
- 在三个公开的超频谱数据集上进行了广泛的实验.
- 与最先进的方法相比,拟议的ESSN方法显示了更高的分类准确性.
- ESSN显示对干扰的免疫力得到改善.
结论:
- ESSN网络有效地提取了HSI分类的深度表示特征.
- 适应边缘特征增强和光谱加权显著提高了性能.
- 对于高光谱图像分类的挑战,ESSN提供了强大而准确的解决方案.
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