摘要高光谱遥感技术是如今非常热门的一个科学领域，在许多领域都发挥着作用。 高光谱图像里含有大量的数据，通过分类算法将其中的数据提取出来进行利用， 这将在民用企业和军用企业上起到至关重要的作用。在高光谱图像中，相邻像元 会包含类似的物质。由于他们的光谱特征具有近似性，可以利用这种特征来提高 分类的准确性。68476
本文在总结高光谱遥感技术和高光谱分类研究现状、分析比较了传统的高光 谱分类方法。在此基础上，研究了基于空间相关性约束的高光谱图像稀疏表示分 类模型和算法，设计了相应的方法和流程，并在 Matlab 平台上开发与实现了高光 谱遥感图像分类软件系统，包括了文件读取、图像分类、精度显示、分类结果比 较等功能，给出了系统及核心模块的详细设计。最后通过实际高光谱数据进行了 详细的比较试验，验证了本文方法和软件功能的有效性。
毕业论文关键词: 高光谱 分类识别 稀疏约束
Title Research on Hyperspectral Image Classification and Identification based on Sparse Constraint.
Abstract
Hyperspectral remote sensing technology is now very popular field of science and has played a role in many fields. Hyperspectral image contains a lot of data, through which the data classification algorithm can extract out for use, and this method will play a crucial role in the civil and military business enterprise. In hyperspectral image, adjacent pixels will contain similar substances. Because of their similarity with the spectral characteristics, this feature can be used to improve the accuracy of classification.
This paper summarizes the research status of hyperspectral remote sensing technology and hyperspectral classification, analysis and compare the traditional high spectral classification. On this basis, the paper makes a research of classification model based on hyperspectral image correlation constraint sparse representation and algorithm, designs the appropriate methods and processes, develops and implement hyperspectral remote sensing image classification system on the Matlab software platforms, includes the file read, image classification, accuracy shows and the classification comparision results etc, gives a detailed system design and kernel modules. Finally, through the actual hyperspectral data for a detailed comparison test , the effectiveness of the methods and software functionality was verified .
Keywords hyperspectral;classification and recognition;sparse constraint
目 录
1 绪论 1
1.1 课题背景 1
1.2 研究意义 1
1.3 国内外现状 2
1.4 论文主要内容 3
2 相关概念与技术 5
2.1 高光谱遥感图像分类 5
2.2 基于稀疏性的高光谱分类 6
2.3 ENVI 软件 6
2.4 Matlab GUI 与矩阵计算 7
3 基于空间相关性约束稀疏表示的高光谱图像分类方法 8
3.1 基于空间相关性约束稀疏表示的算法模型 高光谱数据稀疏约束分类识别技术研究:http://www.751com.cn/jisuanji/lunwen_77013.html