【作者】 路威；

【导师】 余旭初；

【作者基本信息】 中国人民解放军信息工程大学 ， 摄影测量与遥感， 2005， 博士

【摘要】 高光谱遥感技术将反映目标辐射属性的光谱与反映目标空间和几何关系的图像有机地结合在一起。从理论上讲,高光谱遥感信息非常有利于深入挖掘目标的理化特性或精细识别不同目标间的细微差异。但是,传统的全色和多光谱影像的处理方法已无法满足高光谱影像信息提取的需求,面对如此海量的光谱影像数据,人们应如何从中提取感兴趣的信息,是必须解决的问题。 结合国家863-708计划项目,本论文探讨了面向目标探测的高光谱影像特征提取与分类问题。论文以高光谱影像目标探测为主线,重点研究了高光谱影像噪声滤波、小目标探测、小样本分类和非线性特征提取技术。归纳起来,本论文主要在以下几方面开展了开拓性和创新性的研究工作: 1、系统地总结和分析了高光谱影像的结构特性及其对目标探测的影响,探讨了研究高光谱影像特征提取与目标探测时需要考虑的因素和应该注意的问题; 2、较深入地研究了高光谱影像噪声滤波技术,实践了基于三次光滑样条函数和小波函数的高光谱影像自适应滤波方法,并提出了一种改进阈值的多分辨率小波噪声滤波方法。 通过对PHI数据的滤波、信噪比计算和地物分类实验证明,该方法能有效地滤除高光谱影像中随波长变化的噪声,并同时改善数据的信噪比和分类性能; 3、深入研究了面向小目标探测的高光谱影像特征提取技术,提出了基于快速独立成份分析(FICA)、实码遗传优化投影寻踪(RCGAPP)和一维多分辨率小波分析的小目标特征提取方法; ●将FICA引入高光谱影像分析,解决了传统ICA不适用于高光谱影像的问题。并在此基础上,提出了一种基于FICA的高光谱影像小目标特征提取方法。通过AVIRIS和OMIS数据的小目标探测实验可以证明,基于快速独立成份分析(FICA)的特征提取方法能有效地分离出高光谱影像中的非高斯分布结构,准确、高效地对均匀背景地物中分布的小目标进行探测; ●利用实码遗传优化方法解决投影寻踪的指标优化问题,并提出了一种基于RCGAPP的高光谱影像小目标特征提取方法。通过AVIRIS和OMIS数据的小目标探测实验可以证明,RCGAPP方法也可以有效地提取高光谱影像中的小目标信息,并且与ICA方法相比,RCGAPP方法具有更大的灵活性,可以通过设置不同的投影指标而提取不同的兴趣特征; ●基于一维多分辨率小波分析的特征提取方法,是一种面向目标光谱特性的特征提取方法。该方法从地物光谱特性的信息组成结构出发,提取不同尺度上的地物吸收特征,减小光谱数据的冗余性。通过AVIRIS数据的仿真亚像素目标探测实验可以证明,不同尺度上的光谱吸收特征可以很好地表现目标的光谱辐射特性,有效地提高目标探测的效率;更多还原

【Abstract】 Hyperspectral remote sensing effectively integrate the spectral feature with geometric characters of targets. Theoretically speaking, hyperspectral data is greatly propitious to explore target’ s physical and chemical characters deeply or to classify different targets precisely. But conventional data processing methods in panchromatic and multispectral remote sensing can not satisfy demands of hyperspectral data’ s information extraction. In case of so much spectral bands and such huge quantities of data, the key problem is how to extract the interest information.This dissertation explored the theories and methods for feature extraction and classification of hyperspectral data target detection, which are parts of important research contents of National 863-708 Hi-Tech, and concentrated on hyperspectral data target detection, studied de-noising methods of hyperspectral data, small targets detecting methods of hyperspectral data, small training samples classifying of hyperspectral data, nonlinear feature extracting of hyperspectral data mainly. In general, the major works and contribution of this paper are as follows.1 Hyperspectral data’ s characteristic and its effect on target detection are systematically summarized and analyzed. Factor and problem that should be noticed are explored.2 The de-noising techniques of hyperspectral data are studied deeply. Two kinds of methods that based on the cubic smooth spline and stationary discrete wavelet transform respectively are researched, and a new improved threshold de-noising method is brought forward. From noise filtering, signal-to-noise calculating, targets classifying experiments of PHI data, it can be concluded that two methods al 1 can filter the noise in hyperspectral data effectively, which change with wavelengh, and improve data’ s qualities on signal-to-noise and classification.3 The feature extraction techniques for hyperspectral data small targets detection are studied deeply. Three kinds of methods that based on Fast Independent Component Analysis (FICA), Real Coding Genetic Algorithm Projection Pursuit(RCGAPP), Multiscale 1-D Wavelet Transform respectively are brought forward. We settle the problem that ICA is not feet to hyperspectral data with FICA. On the grounds of this, a small targets feature extracting method that based on FICA is brought forward. From small targets detecting experiments of AVIRIS and OMIS data, it can be concluded that feature extraction method, which based on fast independent component analysis (FICA), can extract thenon-gauss structure of hyperspectral data effectively, and is greatly propitious to detect small targets from the background objects that distribute uniformly.? We optimize the projection index with Real Coding Genetic Algorithm, and brought forward a small targets feature extracting method that based on RCGAPP. From small targets detecting experiments of AVIRIS and OMIS data, it can be concluded that projection pursuit can mine the small targets’ information from hyperspectral data. Compared with ICA, it can extract different characteristic structure by set projection index more flexible.? Multiscale 1-D wavelet feature extracting is a method for target’ s spectral characters. It is derived from theories of information structure, can mine the absorbing features on different scales, and reduce quantities of data. From sub. pixel targets detecting experiment of AVIRIS data, it can be concluded that different scale’ s features can describe targets’ spectral characteristic excellent, and improve target detection performance.4> Feature extraction and classification method that based on small training samples learning are studied deeply. A projection index for small training samples learning and a feature extraction and classification method based on SVM projection pursuit is brought forward, and are generalized for multi-class problem by Error Correcting Output Code. From targets classifying experiment of Man-made and AVIRIS data, it can be concluded that this method has an nice ability on small training samples classifying, and can extract the classification feature exactly by little pre-information.5> Training samples kernel-based techniques, which extract nonlinear feature for hyperspectral data, are studied. A feature extraction method that based on kernel Bhattacharyya projection pursuit is researched, and are generalized for multi-class problem by Error Correcting Output Code. From targets classifying experiment of Man-made and AVIRIS data, it can be concluded that kernel-based methods can mine nonlinear feature from hyperspectral data with better performance and simpler calculation.更多还原