基于投影寻踪的多（高）光谱影像分析方法研究
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摘要
遥感对地观测技术是关系到一个国家未来可持续发展的重要技术。目前遥感对地观测技术出现了以下三“高”的发展趋势:高空间分辨率、高时间分辨率和高光谱分辨率。光谱遥感技术逐渐成为人们准确获取地球表面信息的一种主要的手段之一,并发挥着越来越广泛的社会效益,高光谱分辨率传感器是未来空间遥感发展的核心内容。在实际多、高光谱影像处理和分析工作中,光谱影像丰富的光谱信息为地物的边界和地物目标的检测识别创造了良好的条件,随着高光谱影像空间分辨率的提高以及地理信息系统技术的发展,对多、高光谱遥感影像的处理和分析也提出了更高的要求。近年来,对多、高光谱遥感影像处理和分析方法的研究成为遥感领域的前沿之一。将一些新的理论和方法应用于多、高光谱影像处理和分析对于提高遥感信息的提取水平具有十分重要的意义。
投影寻踪是处理和分析高维数据集的一类新兴技术。投影寻踪方法是根据特定的应用意义设计相应的投影指标,把高维数据集投影到低维数据空间后进行分析,揭示高维数据集内部的结构和特征。在多、高光谱遥感影像处理和分析领域中有一个重要的学术思想倾向就是希望在减少光谱数据的维数和数据量,但又期望尽可能保留原始光谱数据的所有信息。这一学术思想倾向与投影寻踪在保留感兴趣信息的前提下,把高维数据集投影到低维数据空间来分析的核心思想不谋而合。本文从投影寻踪降低高维数据集维数的角度出发,将投影寻踪应用于多、高光谱遥感影像分析,主要开展了如下几方面的研究工作:
1、投影寻踪是通过寻找有意义的低维投影以揭示高维数据内部结构特征。本文在分析投影寻踪的数学思想和求解方法基础上,把投影寻踪方法与光谱影像处理分析中降低数据维数的分析方法进行有机的结合。发展了基于全局优化算法(遗传算法、动力演化算法)的投影寻踪方法在多、高光谱影像数据分析中的应用。
2、分析了多、高光谱影像处理和分析时波段选择的必要性和可能性。通过分析多、高光谱影像数据本身具有波段间相关性高的特点,把多、高光谱影像的波段选择看作通用多元数据分析中的变量选择(特征集的选择),采用基于自适应子空间分解的波段选择方法来实现波段选取。并通过波段选取后的多、高光谱影像分类试验来验证基于自适应子空间分解的波段选择方法的优点和有效性。
3、主成分分析方法在多、高光谱影像处理和分析中得到了广泛的应用,本文在分析主成分分析投影寻踪思想的基础上,通过比较投影寻踪方法与一般解析方法求解样本数据主成分的试验,验证两种求解方法在本质上的一致性。在寻找最佳投影方向上,发展了标准遗传算法,提出基于动力演化的优化算法。选择信息散度作为投影指标,并通过模拟数据和多光谱影像数据试验表明了该方法在提取高维数据集非正态性结构特征方面的优势。
4、在分析线性光谱混合模型应用于遥感影像混合像元分离的基础上,把多、高光谱影像当作观测信号,看作由各个独立信号源混合而成。根据观测混合信号比各个独立信号更接
The earth observation technology is critical for national future sustainable development. With the tendencies of "3-High" (high spatial resolution, high frequency resolution and high spectral resolution), Spectral remote sensing has become one major technique to achieve earth surface information and has become more and more important for our lives and social. In processing and analyzing multi-spectral (Hyper-spectral) images the sufficient spectral information of images provide beneficial conditions to detect and recognize objects. But they also present more requests on processing and analyzing multi-spectral (Hyper-spectral) remote sensing images. Nowadays the researches of Hyper-spectral remote sensing images are active and have achieved considerable successes. The application of new theories and methods on processing and analyzing multi-spectral (Hyper-spectral) remote sensing images should have significant meanings.Projection pursuit is a new technology to process and analysis high dimensional data. By designing projection index it projects high dimensional data set to low dimensional space to reveal the internal structures and characters of high dimensional dataset. In processing multi-spectral (Hyper-spectral) remote sensing images, an important idea is to reduce data dimension and data amount and at the same time to retain as more information as possible which contained in the original data sources. This just coincides with the heart spirit of Projection Pursuit. This paper applies Projection Pursuit to the analysis of multi-spectral (Hyper-spectral) remote sensing images. The research work is represented as follows.1. Projection Pursuit is applied to explore the potential structures and characters of the multi-dimension data through projecting the high dimensional data set into a low dimensional data space while retaining the information of interest. While in processing and analyzing spectral images a common used method is to reduce the data dimension. Combined the two methods reasonably this paper developed the Projection Pursuit method based on global optimize algorithm used to the analysis of multi-spectral (Hyper-spectral) images.2. Analyzed the significance and possibility of band selection in processing multi-spectral (Hyper-spectral) remote sensing images. Because of the character of strong correlations among spectral bands, the bands selection of spectral images can be seemed as variable selection of general multivariate data analysis. This paper
developed the band selection method based on self-adaptive subspace decomposition. The advantages and effectiveness of this algorithm are verified by the experiments on classification of multi-spectral (Hyper-spectral) remote sensing images.3. Principal Component Analysis (PCA) is used widely in multi-spectral (Hyper-spectral) remote sensing image processing and analyzing. There are two methods to compute PCA. One is Projection Pursuit which based on genetic algorithm to compute PCA. The other uses the general analytical method to compute PCA. After analyzed the general meaning of Projection Pursuit and compared the two methods this paper developed the standard genetic algorithm and proposed optimize algorithm based on dynamical evolution to finding the optimal projection index. Let the information divergence as projection index, the advantages of this method on extraction high dimensional non-normal distribution data set are proved by the experiments on analog data and multi-spectral images data.4. Linear spectral mixture model is a most used method on mixture pixel separation. This paper assumed the multi-spectral (Hyper-spectral) remote sensing image as observed signals and was generated by mixture of various independent signals. Because the mixture signals are more close to normal distribution than each independent signal this paper proposed mixture pixel non-supervised classification based on Independent Component Analysis (ICA). And negative entropy is used as estimation standard of dependent. The method is successfully used on analog data separation and multi-spectral images non-supervised classification.5. Based on the band selection results, the projection index function of high order statistics is selected and designed through Projection Pursuit based on genetic algorithm (dynamical evolution algorithm). This paper developed the zero threshold detection method to detect the abnormity objects in hyper-spectral images. At last the computation advantages of Projection Pursuit based on dynamical evolution algorithm is validated by experiments.Projection Pursuit technology combined the Projection Pursuit method with the field of processing and analyzing multi-spectral (Hyper-spectral) remote sensing images. There are a lot of theme should be researched in the future, including spectral matching, mixture spectral separation, spectral classification, spectral data compression and spectral object recognition etc.
投影寻踪是处理和分析高维数据集的一类新兴技术。投影寻踪方法是根据特定的应用意义设计相应的投影指标,把高维数据集投影到低维数据空间后进行分析,揭示高维数据集内部的结构和特征。在多、高光谱遥感影像处理和分析领域中有一个重要的学术思想倾向就是希望在减少光谱数据的维数和数据量,但又期望尽可能保留原始光谱数据的所有信息。这一学术思想倾向与投影寻踪在保留感兴趣信息的前提下,把高维数据集投影到低维数据空间来分析的核心思想不谋而合。本文从投影寻踪降低高维数据集维数的角度出发,将投影寻踪应用于多、高光谱遥感影像分析,主要开展了如下几方面的研究工作:
1、投影寻踪是通过寻找有意义的低维投影以揭示高维数据内部结构特征。本文在分析投影寻踪的数学思想和求解方法基础上,把投影寻踪方法与光谱影像处理分析中降低数据维数的分析方法进行有机的结合。发展了基于全局优化算法(遗传算法、动力演化算法)的投影寻踪方法在多、高光谱影像数据分析中的应用。
2、分析了多、高光谱影像处理和分析时波段选择的必要性和可能性。通过分析多、高光谱影像数据本身具有波段间相关性高的特点,把多、高光谱影像的波段选择看作通用多元数据分析中的变量选择(特征集的选择),采用基于自适应子空间分解的波段选择方法来实现波段选取。并通过波段选取后的多、高光谱影像分类试验来验证基于自适应子空间分解的波段选择方法的优点和有效性。
3、主成分分析方法在多、高光谱影像处理和分析中得到了广泛的应用,本文在分析主成分分析投影寻踪思想的基础上,通过比较投影寻踪方法与一般解析方法求解样本数据主成分的试验,验证两种求解方法在本质上的一致性。在寻找最佳投影方向上,发展了标准遗传算法,提出基于动力演化的优化算法。选择信息散度作为投影指标,并通过模拟数据和多光谱影像数据试验表明了该方法在提取高维数据集非正态性结构特征方面的优势。
4、在分析线性光谱混合模型应用于遥感影像混合像元分离的基础上,把多、高光谱影像当作观测信号,看作由各个独立信号源混合而成。根据观测混合信号比各个独立信号更接
The earth observation technology is critical for national future sustainable development. With the tendencies of "3-High" (high spatial resolution, high frequency resolution and high spectral resolution), Spectral remote sensing has become one major technique to achieve earth surface information and has become more and more important for our lives and social. In processing and analyzing multi-spectral (Hyper-spectral) images the sufficient spectral information of images provide beneficial conditions to detect and recognize objects. But they also present more requests on processing and analyzing multi-spectral (Hyper-spectral) remote sensing images. Nowadays the researches of Hyper-spectral remote sensing images are active and have achieved considerable successes. The application of new theories and methods on processing and analyzing multi-spectral (Hyper-spectral) remote sensing images should have significant meanings.Projection pursuit is a new technology to process and analysis high dimensional data. By designing projection index it projects high dimensional data set to low dimensional space to reveal the internal structures and characters of high dimensional dataset. In processing multi-spectral (Hyper-spectral) remote sensing images, an important idea is to reduce data dimension and data amount and at the same time to retain as more information as possible which contained in the original data sources. This just coincides with the heart spirit of Projection Pursuit. This paper applies Projection Pursuit to the analysis of multi-spectral (Hyper-spectral) remote sensing images. The research work is represented as follows.1. Projection Pursuit is applied to explore the potential structures and characters of the multi-dimension data through projecting the high dimensional data set into a low dimensional data space while retaining the information of interest. While in processing and analyzing spectral images a common used method is to reduce the data dimension. Combined the two methods reasonably this paper developed the Projection Pursuit method based on global optimize algorithm used to the analysis of multi-spectral (Hyper-spectral) images.2. Analyzed the significance and possibility of band selection in processing multi-spectral (Hyper-spectral) remote sensing images. Because of the character of strong correlations among spectral bands, the bands selection of spectral images can be seemed as variable selection of general multivariate data analysis. This paper
developed the band selection method based on self-adaptive subspace decomposition. The advantages and effectiveness of this algorithm are verified by the experiments on classification of multi-spectral (Hyper-spectral) remote sensing images.3. Principal Component Analysis (PCA) is used widely in multi-spectral (Hyper-spectral) remote sensing image processing and analyzing. There are two methods to compute PCA. One is Projection Pursuit which based on genetic algorithm to compute PCA. The other uses the general analytical method to compute PCA. After analyzed the general meaning of Projection Pursuit and compared the two methods this paper developed the standard genetic algorithm and proposed optimize algorithm based on dynamical evolution to finding the optimal projection index. Let the information divergence as projection index, the advantages of this method on extraction high dimensional non-normal distribution data set are proved by the experiments on analog data and multi-spectral images data.4. Linear spectral mixture model is a most used method on mixture pixel separation. This paper assumed the multi-spectral (Hyper-spectral) remote sensing image as observed signals and was generated by mixture of various independent signals. Because the mixture signals are more close to normal distribution than each independent signal this paper proposed mixture pixel non-supervised classification based on Independent Component Analysis (ICA). And negative entropy is used as estimation standard of dependent. The method is successfully used on analog data separation and multi-spectral images non-supervised classification.5. Based on the band selection results, the projection index function of high order statistics is selected and designed through Projection Pursuit based on genetic algorithm (dynamical evolution algorithm). This paper developed the zero threshold detection method to detect the abnormity objects in hyper-spectral images. At last the computation advantages of Projection Pursuit based on dynamical evolution algorithm is validated by experiments.Projection Pursuit technology combined the Projection Pursuit method with the field of processing and analyzing multi-spectral (Hyper-spectral) remote sensing images. There are a lot of theme should be researched in the future, including spectral matching, mixture spectral separation, spectral classification, spectral data compression and spectral object recognition etc.
引文
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