详细介绍了高光谱遥感的基础和分析方法，是学习和考试的好资料 详细介绍了高光谱遥感的基础和分析方法，是学习和考试的好资料

高光谱遥感图像MATLAB工具箱 程序齐全 非常好用！！！

The NASA EO-1 satellite acquired a sequence of data over the Okavango Delta, Botswana in 2001-2004. The Hyperion sensor on EO-1 acquires data at 30 m pixel resolution over a 7.7 km strip in 242 bands covering the 400-2500 nm portion of the spectrum in 10 nm windows. Preprocessing of the data was performed by the UT Center for Space Research to mitigate the effects of bad detectors, inter-detector miscalibration, and intermittent anomalies. Uncalibrated and noisy bands that cover water absorption features were removed, and the remaining 145 bands were included as candidate features: [10-55, 82-97, 102-119, 134-164, 187-220]. The data analyzed in this study, acquired May 31, 2001, consist of observations from 14 identified classes representing the land cover types in seasonal swamps, occasional swamps, and drier woodlands located in the distal portion of the Delta.

文章对高光谱遥感技术的发展概况进行了回顾，详细介绍了典型的高光谱遥感仪器的发展历程及其主要参数，对比了不同时期各个国家高光谱遥感载荷的性能特点，分析了中国高光谱遥感技术发展现状，并归纳了国际上未来高光谱遥感技术发展计划。文章结合当前信息时代的发展特点，对高光谱遥感技术未来发展进行了展望，指出了高光谱遥感技术探测波段进一步拓宽，时间、空间及光谱分辨率进一步提高，高光谱遥感技术种类进一步丰富，图像、光谱、偏振多元信息一体化获取，智能化、网络化以及小型轻量化的发展趋势，可为中国高光谱遥感技术的进一步成熟化和实用化提供参考。

《高光谱遥感及其应用》——浦瑞良、宫鹏

完全用Excel函数写的包络线去除分析，仅实现了1850—2100nm的包络线去除，其他波段可自行扩展……

高光谱遥感图像的小波去噪方法pdf

高光谱遥感分类数据集，PaviaUniversity。文件中有.mat格式的数据和ENVI原影像。如需要其他高光谱遥感分类数据集可与本人联系。

These are two scenes acquired by the ROSIS sensor during a flight campaign over Pavia, nothern Italy. The number of spectral bands is 102 for Pavia Centre and 103 for Pavia University. Pavia Centre is a 1096*1096 pixels image, and Pavia University is 610*610 pixels, but some of the samples in both images contain no information and have to be discarded before the analysis. The geometric resolution is 1.3 meters. Both image groundtruths differenciate 9 classes each. It can be seen the discarded samples in the figures as abroad black strips.

气溶胶光学厚度(Aerial Optical Depth,AOD)是重要的大气参数,也是大气校正中不可缺少的变量。文中通过两种AOD反演算法的实施和比较,探讨了利用高光谱进行AOD反演的可行性和算法的适用性。通过两种方法,对四幅影像进行了气溶胶的反演,并以地基观测数据进行了对比验证。结果显示改进的V5.2算法在非浓密植被区域的精度较高,而在浓密植被区域与DDV算法精度相当,由于两种算法均依赖于波段固定的线性关系,使得两种算法在气溶胶的反演中均有误差。