本研究回顾了基于主成分分析PCA和判别分析LDA的降维方法及其扩展，包括经典主成分分析、概率主成分分析、核主成分分析，以及线性判别分析、局部保持降维、图形嵌入判别分析和半监督降维分析。

结合高光谱数据和深度学习的特点，提出一种同时考虑像素光谱信息和空间信息的深度卷积神经网络框架。 该框架主要步骤如下：首先利用主成分分析法对高光谱遥感图像进行光谱特征提取，消除特征之间的相关性，并降低特征维数，获得清晰的空间结构；然后利用深度卷积神经网络对输入的样本进行空间特征提取；最后通过学 习到的高级特征进行 回归训练

进行高光谱图像处理时的降维程序，修改文件中的读入参数名称即可使用。

matlab cnn高光谱图像分类

The NASA AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument acquired data over the Kennedy Space Center (KSC), Florida, on March 23, 1996. AVIRIS acquires data in 224 bands of 10 nm width with center wavelengths from 400 - 2500 nm. The KSC data, acquired from an altitude of approximately 20 km, have a spatial resolution of 18 m. After removing water absorption and low SNR bands, 176 bands were used for the analysis. Training data were selected using land cover maps derived from color infrared photography provided by the Kennedy Space Center and Landsat Thematic Mapper (TM) imagery. The vegetation classification scheme was developed by KSC personnel in an effort to define functional types that are discernable at the spatial resolution of Landsat and these AVIRIS data. Discrimination of land cover for this environment is difficult due to the similarity of spectral signatures for certain vegetation types. For classification purposes, 13 classes representing the various land cover types that occur in this environment were defined for the site.

一组标准的高光谱数据，以及自己编写 multibandread（）函数读取的matlab程序，和大家分享，应该对大家有帮助

非常好用的高光谱图像处理工具，可以直接使用，也可以在此基础上进行二次开发。好东西啊。

高光谱论文\非监督的高光谱图像解混技术研究

【项目代码】利用MATLAB对高高光谱图像数据进行分析，程序很全面，对做高光谱的同志很有帮助哦.rar

matlab实现用cnn高光谱图像分类，