【作者】 廖文志；

【导师】 皮佑国； Wilfried Philips； Aleksandra Pizurica；

【作者基本信息】 华南理工大学 ， 控制理论与控制工程， 2012， 博士

【摘要】 超光谱遥感图像将反映目标的光谱信息与反映目标空间和几何关系的图像信息有机结合,具有“谱图合一”的特点,能够有效地提高对地物的分类和监测能力,已经被广泛应用于资源勘探、环境监测、灾害评估、精细农业、目标识别等领域且应用领域还在不断扩大。随着科学技术的进步,图像光谱分辨率不断提高,所获取的超光谱遥感图像数据量也越来越大,波段间相关性增高。另外,不同的地物可能具有相同的光谱反射(光谱信息),仅从光谱信息上去识别不同的地物很难得到满意的结果。与中低空间分辨率图像相比,高空间分辨率超光谱遥感图像的信息更加丰富,不仅包含地物的光谱信息,而且还包含了地物的大量结构、纹理、形状等细节信息。使用传统方法对超光谱遥感图像进行分类时,存在小样本问题-训练样本不足、参数估计不可靠,导致数据维数灾难等问题的发生。因此,研究超光谱遥感图像特征提取和分类的方法及技术具有重要的理论意义和广阔的应用前景。本文在对现有的超光谱遥感图像特征提取方法和技术进行分析和研究的基础上,重点研究超光谱遥感图像的光谱特征和空间特征提取方法,并将其有机融合,有效提高超光谱遥感图像的分类精度和效率,主要研究内容及贡献为：1.针对超光谱遥感图像中的小样本问题,提出了一种结合局部信息和类别信息的半监督特(SELD)超光谱遥感图像征提取方法。该方法将无监督的特征提取方法(LLFE)和有监督的特征提取方法(LDA)有机结合,为超光谱遥感图像的半监督特征提取方法提供了一种新框架。其中超光谱遥感数据的局部邻域信息是通过SELD中无监督部分(LLFE)利用大量无标识样本来提取与保留；而类别信息则通过SELD中有监督部分(LDA)利用少量有标识样本来实现。实验表明SELD在降低超光谱遥感图像的维数的同时,提取和保留了图像中的局部邻域信息及类别信息,能够有效地提高超光谱遥感图像的分类精度和效率。2.针对高空间分辨率超光谱城市遥感图像,应用了数学形态学矢量的部分重构技术来提取图像中空间信息。利用传统的数学形态学算子进行开和闭运算能够有效的检测图像中的空间信息(图像中物体的大小和形状信息),当结构算子的尺度不断增加时,开和闭运算使得图像中越来越多的物体逐渐的消失,其中结构算子的形状对应于图像中物体的形状,而结构算子的尺度对应于物体的大小。然而,传统的开和闭运算容易引起图像中物体形状的改变。常用的数学形态学重构是一个完全重构的过程,能够很好的解决这个问题,使得图像中物体的形状得以较好的保存。但是随着尺度的增加,图像中一些本该消失小物体因为重构却依然保留在图像中,使得图像中物体的大小信息不能得到很好的利用。应用数学形态学中的部分重构技术,仅对图像中满足一定条件的像素进行重构,随着结构算子的尺度增加,图像中的物体在其对应的尺度中逐渐消失,不仅能保留图像中物体的形状信息,而且能保留物体的大小信息。3.将光谱信息和空间信息融合并利用数学形态学矢量统一表达,能充分利用超光谱遥感图像的光谱特征和空间形态特征。但矢量维数地不断增加,导致计算量增加和小样本问题的出现。因此提出了一种半监督的数学形态学矢量特征提取方法(GSELD),并首次将半监督的特征提取方法应用到超光谱数学形态学矢量当中。首先利用主成份分析法从超光谱图像中提取若干主成份(特征值累积99%)来充分利用图像的光谱信息,并在选取的每一个主成份中利用不同的结构算子和尺度构造数学形态学矢量。这些矢量中包含了图像的空间信息图像中物体的大小和形态信息。然而所产生的数学形态学矢量是一个高维数据,且冗余度高。GSELD利用有限的训练样本不仅可以从高维的数学形态学矢量中提取出有效的特征,降低数据的维数,而且能够有效地提高各种分类器的分类精度和效率。4.提出了一种针对超光谱遥感图像的非线性特征提取方法快速迭代核主成份分析(FIKPCA)方法。在实际应用当中,线性的特征提取方法在对数据降维后将会丢失数据中的非线性的特性。而传统的非线性的方法存在运算量大、内存消耗大、计算速度慢等问题。FIKPCA通过迭代的方法来求解特征向量,而不需要对Gram矩阵进行特征分解,可以极大地降低时间复杂度和空间复杂度,从而提高了非线性特征提取方法在超光谱遥感图像处理中的运算效率。5.提出了一种在非线性特征上构造数学形态学矢量的方法。在利用从高维超光谱遥感图像提取出来的线性特征构造数学形态学矢量时,图像中像素间的非线性关系在特征提取和降维过程中常常被忽略。核主成份作为非线性特征能更好地利用数据中的高阶统计信息和非线性关系,利用核主成份构造数学形态学矢量能明显地提高超光谱遥感图像的分类精度和效率。本文对超光谱遥感图像的光谱特征提取和分类、空间特征提取和分类进行了深入的研究探讨。提出了结合局部信息和类别信息的半监督特(SELD)超光谱遥感图像特征提取方法和利用数学形态学矢量的部分重构技术的图像空间特征提取方法。将超光谱遥感图像中的光谱信息和空间信息融合并统一表达为数学形态学矢量。针对高维形态学矢量的降维和特征提取提出了一种半监督的数学形态学矢量特征提取方法(GSELD)。对非线性特征,提出了快速迭代核主成份分析(FIKPCA)方法和一种在非线性特征上构造数学形态学矢量的方法。仿真试验和大量实际超光谱遥感图像实验表明,本文提出的特征提取方法效果优于相关方法,能够有效地提高超光谱遥感图像的分类精度和效率。更多还原

【Abstract】 Recent advances in sensor technology have led to an increased availability of hyper-spectral remote sensing data at very high both spectral and spatial resolutions. Manytechniques are developed to explore the spectral information and the spatial informationof these data. In particular, feature extraction (FE) aimed at reducing the dimensionalityof hyperspectral data while keeping as much spectral information as possible is one ofmethods to preserve the spectral information, while morphological profile analysis is themost popular methods used to explore the spatial information.Hyperspectral sensors collect information as a set of images represented by hun-dreds of spectral bands. While ofering much richer spectral information than regularRGB and multispectral images, the high dimensional hyperspectal data creates also achallenge for traditional spectral data processing techniques. Conventional classifica-tion methods perform poorly on hyperspectral data due to the curse of dimensionality(i.e. the Hughes phenomenon: for a limited number of training samples, the classifica-tion accuracy decreases as the dimension increases). Classification techniques in patternrecognition typically assume that there are enough training samples available to obtainreasonably accurate class descriptions in quantitative form. However, the assumptionthat enough training samples are available to accurately estimate the class description isfrequently not satisfied for hyperspectral remote sensing data classification, because thecost of collecting ground-truth of observed data can be considerably difcult and expen-sive. In contrast, techniques making accurate estimation by using only small trainingsamples can save time and cost considerably. The small sample size problem thereforebecomes a very important issue for hyperspectral image classification.Very high-resolution remotely sensed images from urban areas have recently becomeavailable. The classification of such images is challenging because urban areas oftencomprise a large number of diferent surface materials, and consequently the heterogeneityof urban images is relatively high. Moreover, diferent information classes can be made upof spectrally similar surface materials. Therefore, it is important to combine spectral andspatial information to improve the classification accuracy. In particular, morphological profile analysis is one of the most popular methods to explore the spatial informationof the high resolution remote sensing data. When using morphological profiles (MPs)to explore the spatial information for the classification of hyperspectral data, one shouldconsider three important issues. Firstly, classical morphological openings and closingsdegrade the object boundaries and deform the object shapes, while the morphologicalprofile by reconstruction leads to some unexpected and undesirable results (e.g. over-reconstruction). Secondly, the generated MPs produce high-dimensional data, which maycontain redundant information and create a new challenge for conventional classificationmethods, especially for the classifiers which are not robust to the Hughes phenomenon.Last but not least, linear features, which are used to construct MPs, lose too muchspectral information when extracted from the original hyperspectral data.In order to overcome these problems and improve the classification results, we de-velop efective feature extraction algorithms and combine morphological features for theclassification of hyperspectral remote sensing data. The contributions of this thesis areas follows.1. As the first contribution of this thesis, a novel semi-supervised local discriminantanalysis (SELD) method is proposed for feature extraction in hyperspectral remotesensing imagery, with improved performance in both ill-posed and poor-posed condi-tions. The proposed method combines unsupervised methods (Local Linear FeatureExtraction Methods (LLFE)) and supervised method (Linear Discriminant Analy-sis (LDA)) in a novel framework without any free parameters. The underlying ideais to design an optimal projection matrix, which preserves the local neighborhoodinformation inferred from unlabeled samples, while simultaneously maximizing theclass discrimination of the data inferred from the labeled samples.2. Our second contribution is the application of morphological profiles with partialreconstruction to explore the spatial information in hyperspectral remote sensingdata from the urban areas. Classical morphological openings and closings degradethe object boundaries and deform the object shapes. Morphological openings andclosings by reconstruction can avoid this problem, but this process leads to someundesirable efects. Objects expected to disappear at a certain scale remain presentwhen using morphological openings and closings by reconstruction, which meansthat object size is often incorrectly represented. Morphological profiles with partialreconstruction improve upon both classical MPs and MPs with reconstruction. Theshapes of objects are better preserved than classical MPs and the size information is preserved better than in reconstruction MPs.3. A novel semi-supervised feature extraction framework for dimension reduction ofgenerated morphological profiles is the third contribution of this thesis. The mor-phological profiles (MPs) with diferent structuring elements and a range of in-creasing sizes of morphological operators produce high-dimensional data. Thesehigh-dimensional data may contain redundant information and create a new chal-lenge for conventional classification methods, especially for the classifiers which arenot robust to the Hughes phenomenon. To the best of our knowledge the use ofsemi-supervised feature extraction methods for the generated morphological profileshas not been investigated yet. The proposed generalized semi-supervised local dis-criminant analysis (GSELD) is an extension of SELD with a data-driven parameter.4. In our fourth contribution, we propose a fast iterative kernel principal componentanalysis (FIKPCA) to extract features from hyperspectral images. In many ap-plications, linear FE methods, which depend on linear projection, can result inloss of nonlinear properties of the original data after reduction of dimensional-ity. Traditional nonlinear methods will cause some problems on storage resourcesand computational load. The proposed method is a kernel version of the CandidCovariance-Free Incremental Principal Component Analysis, which estimates theeigenvectors through iteration. Without performing eigen decomposition on theGram matrix, our approach can reduce the space complexity and time complexitygreatly.5. Our last contribution constructs MPs with partial reconstruction on nonlinear fea-tures. Traditional linear features, on which the morphological profiles usually arebuilt, lose too much spectral information. Nonlinear features are more suitable todescribe higher order complex and nonlinear distributions. In particular, kernelprincipal components are among the nonlinear features we used to built MPs withpartial reconstruction, which led to significant improvement in terms of classifica-tion accuracies.The experimental analysis performed with the novel techniques developed in thisthesis demonstrates an improvement in terms of accuracies in diferent fields of applicationwhen compared to other state of the art methods.更多还原