使用半监督学习算法对超光谱遥感的图像分类
Ansheng Ye1,2, Xiangbing Zhou3, Kai Weng4
1Key Lab of Earth Exploration & Information Techniques of Ministry Education, Chengdu University of Technology, Chengdu 610059, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
本研究介绍了一种使用局部二进制模式 (LBP) 进行纹理分析和半监督学习的新型超光谱图像分类方法. 该方法提高了远程传感数据的分类准确性和效率.
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:
- 超光谱图像提供丰富的光谱和空间数据,但在处理,分析和标记方面存在挑战.
- 对各种应用而言,对超光谱遥感数据的准确分类至关重要.
研究的目的:
- 开发一种有效的超光谱图像分类方法,利用纹理特征和半监督学习.
- 解决样品标签方面的困难,提高分类准确性.
主要方法:
- 利用局部二进制模式 (LBP) 来从高光谱图像中提取空间纹理特征.
- 实施了一种半监督学习方法,结合邻里信息和伪标签的优先分类器歧视.
- 采用稀疏表示和混合后勤回归来进行可靠的分类.
主要成果:
- 与现有方法相比,拟议的方法实现了更高的分类准确性.
- 在基准数据集 (印度松树,萨利纳斯,帕维亚大学) 上表现出更强的及时性和改进的概括能力.
结论:
- 新的分类方法有效地利用纹理特征和半监督学习来进行准确的超光谱图像分析.
- 该方法为提高超光谱遥感图像分类的效率和准确性提供了一个有希望的解决方案.
相关概念视频
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