摘要
本论文主要以我国首台业务运行星载光谱仪数据作为数据源，针对高光谱数据处理过程中的几个关键问题进行分析，提出解决方案和具体方法，为高光谱数据处理及应用研究工作奠定基础。
本论文的主要研究内容包括：
1.对高光谱遥感技术进行归纳总结。介绍了高光谱遥感的概念，应用、光谱成像仪的分类及数据处理技术。
2.针对目前仅从图像信息保持能力进行条带去除算法评价的不足，提出条带去除算法的光谱信息保持能力评价标准。在分析干涉式成像光谱仪条带噪声产生机理的基础上，提出了可用于HJ-1A超光谱成像仪条带噪声去除的二次灰度系数校正法，该方法在图像信息保持能力和光谱信息保持能力上优于传统算法。比较了实验室定标和星上定标的差异，提出一种有利于条带噪声消除的推扫型干涉成像光谱仪辐射场绝对辐射定标方法。
3.分析了使用FLAASH和QUAC进行HJ-1A HSI数据大气校正的效果，提出一种基于厚云光谱的快速大气校正方法(BCSQUAC)，该方法在保证大气校正精度的情况下，具有远优于QUAC的速度。
4.以光谱距离公式为基础，推导了光谱距离随谱段增加的变化趋势，通过在波段增加过程中控制光谱距离的变化趋势来达到进行最优波段选择的目的。提出可用于多种光谱识别的最优波段选择方法。
5.为了修正由于光谱信息的不确定性引起的分类错误，本文提出一种基于空间特征的监督分类算法，利用该算法对HJ-1A超光谱成像仪数据分类，得到优于SAM算法的效果。将基于空间特征的非监督分类算法进行改进后作为端元提取的预处理操作，避免从所有像元中提取端元，极大地提高端元提取的速度。
Based on the data source produced by the first national spacebornehyperspectral imaging spectrometer in business operation, this dissertation mainlystudies several key problems in the hyperspectral data processing, and then proposesassociated solutions and methodologies, which could be bases for hyperspectral dataprocessing and application.
The contents mainly include the following contexts:
1. The remote sensing technologies for hyperspectral are summarized. Theconcepts and applications of hyperspectral remote sensing, classification ofimaging spectrometers and technologies of data processing are brieflydescribed.
2. As it is partial to assess destriping algorithm only from the aspect ofmaintenance of image information currently, a new criterion is proposed toassess the ability of destriping algorithm to maintain the spectral information.Based on analyzing the generating mechanism of strips in the data ofinterference imaging spectrometers, a method for image processing isproposed to eliminate the strip in the data of HJ-1A HSI, which producedbetter results in the maintenance of image information and spectralinformation. The differences are compared between calibration methods ofHJ-1A HSI in library and On-Orbit, and than a new method for absoluteradiance calibration is presented to destrip the image of push-broominterference imaging spectrometers.
3. The performance of FLAASH and QUAC to correct atmosphere in HJ-1AHSI data is analyzed, and then a quick atmospheric correction method basedon the characteristic spectrum of thick clouds is presented. With qualifiedaccuracy in atmospheric correction, the method acquires much higher speedthan QUAC.
4. Based on the formula of spectral distance, a variation trend of spectraldistance with increasing bands is deduced. The trend is used to optimizebands selection by controlling the change direction of the average spectraldistances among materials. An optimization method for band selection isproposed in the spectral discrimination.
5. In order to correct the errors in classification produced by the uncertainty ofspectral information, an algorithm of supervised spectral classification basedon the spatial information is presented. This algorithm produced better resultsthan that of the SAM algorithm as used to classify the spectral data of HJ-1AHSI. An improved algorithm of unsupervised spectral classification based onspatial information is used as a preprocessing of endmember extraction,where the endmember is extracted from one pixel classes rather than from allthe pixels in the image, which greatly improve the speed of algorithm.
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