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高光谱遥感图像的特征选择和分类算法研究
李海昌
学位类型工学博士
导师向世明 研究员 ; 潘春洪 研究员
2016-05-27
学位授予单位中国科学院大学
学位授予地点北京
学位专业模式识别与智能系统
关键词高光谱遥感图像 地物目标分类 波段选择 特征选择 稀疏学习 半监督学习 标签传播 流形正则化
摘要高光谱遥感图像地物目标分类是遥感图像处理领域的研究热点之一,是发展高光谱图像分析系统的重要技术,同时也是众多高光谱应用的基础。高光谱图像地物目标分类任务主要涉及高光谱波段选择、地物目标特征描述和分类器构造等多个方面。高光谱图像地物目标分类仍然是一个挑战性问题,主要源于如下几方面的难点:(1)高光谱遥感图像光谱分辨率高,但容易受噪声干扰,产生诸多外点通道(波段),采用所有波段图像数据会影响地物目标分类器的构造;(2)高光谱遥感图像空间分辨率低,图像中往往存在诸多小块区域目标,且同一类地物目标通常分布在不同的空间位置,但标注所有小区域费时费力,通常仅有少量标注样本;(3)在仅有少量标注的高光谱图像地物目标样本且样本维数较高时,分类过程中容易产生“Hughes”现象,影响分类精度的进一步提高。本文针对上述诸问题,从稀疏学习、无监督学习和半监督学习的角度开展波段选择、多特征选择、地物目标分类器构造等相关研究。具体而言,论文的主要工作和主要贡献总结如下:
(i) 提出了一种基于结构稀疏学习和半监督学习的高光谱遥感图像波段选择方法。其核心思想是将波段选择视为一个特征选择任务,通过引入结构稀疏约束和流形正则约束来提升波段选择的鲁棒性和精度。一方面,在光谱波段层面,通过引入基于矩阵ℓ2,1范数的结构稀疏约束来提升模型对噪声波段的鲁棒性;另一方面,在图像空间层面,通过引入流形正则约束来描述高光谱图像空间近邻像素类别标签的局部平滑性。该模型所选波段具有较好的分类判别能力。对比实验验证了所提方法的有效性。
(ii) 提出了一种多视图无监督高光谱图像特征选择方法。其核心思想是采用多视图低维嵌入方法将光谱特征和高光谱图像地物目标低层视觉特征映射至低维嵌入表示。在特征选择模型构建方面,采用ℓ2,1 结构稀疏形式的鲁棒线性回归来保持该低维嵌入信息。该建模方案充分利用了多种类型特征的互补信息;同时,由于所引入的低维嵌入表示在一定程度上隐含地保持了数据的聚类信息和判别信息,故所选特征有更强的分类能力。对比实验验证了所提方法的有效性。
(iii) 对于少量训练样本且样本特征维度较高的高光谱遥感图像地物目标分类任务,一种典型的处理方案是基于图的标签传播方法,以利用大量的未标注样本的信息。但是,由于高光谱遥感图像空间分辨率较低,图像中有许多小的区域目标,这些小的区域会在标注样本直接进行标签传播中被平滑掉,并显著地影响分类精度。为此,本文提出了一种可以自动标注样本的半监督高光谱遥感图像地物目标分类方法。首先,基于已有少量标注样本所训练的分类器对高光谱遥感图像的像素级分类结果和局部区域类别标签一致性准则选择新的种子点,并将所选种子点的类别标签概率在图像空间中进行传播。其次,构建可保持局部边缘特性的图标签传播半监督分类模型。最后,将标签传播问题形式化地表述为一个能量最小化模型并通过求解稀疏线性方程组的方法获得分类结果。对比研究表明,在分类结果上,本文所提出的方法优于现有方法。
 
其他摘要
Target (land cover) classification for hyperspectral images is a hot topic in remote sensing image processing. It is an important and fundamental technique for developing hyperspectral image analyzing systems in various types of applications. Technically, the task of target classification for hyperspectral images is involved the following issues: band selection, feature representation and classifier construction. Although there are many thoughtful attempts in the past years, it is still a challenging problem due to the following three reasons. (1) The spectral resolution of hyperspectral images is usually high, and the images could be easily degraded by various kinds of noises, resulting in many outlier channels (bands). Employing all of the spectral bands to train a classifier could reduce the classification performance. (2) The spatial resolution of hyperspectral images is usually
low and many small targets belong to the same classes often locate in different spatial regions. It is laborious to label all of the small regions (especially for those targets spatially located in hundreds of thousands tiny regions) for training a desired classifier. (3) Given a small number of labeled samples with high dimensionality, “Hughes”phenomenon in training and prediction may occur, which could finally degrade the classification accuracy. Taking the above aspects into consideration, in this dissertation, we will focus on the following issues: band selection, multiple feature selection and classifier construction in terms of sparse learning, unsupervised learning and semi-supervised learning. Specifically, the main contributions are listed as follows:
(i) A band selection approach based on the structured sparse learning and semi-supervised learning is proposed. Specifically, the core idea is to consider the task of band selection as a problem of feature selection, which incorporates the structured sparse constraint and manifold regularization to improve the robustness and accuracy of band selection. On the one hand, in the level of spectral bands, a ℓ2,1 norm structured sparse constraint is incorporated into the model to improve its robustness to noise bands. On the other hand, in the level of spatial pixels, a trick of manifold regularization is employed to describe the local smoothness for spatial neighbors. As a result, the selected bands by the proposed method have better discriminative ability for land cover classification. Comparative experiments validate the effectiveness of the proposed method.
(ii) A multi-view unsupervised feature selection approach is proposed for multiple features of hyperspectral images. Specifically, the core idea is to map the spectral features and low-level visual features of targets into a low dimensional embedding representation using the trick of multi-view embedding. To achieve this goal, a ℓ2,1 structured sparse robust linear regression model is constructed to preserve the low dimensional embedding structures. As a result, the proposed model explores and exploits the complementary information contained in the multiple features. Meanwhile, the low dimensional embedding representation preserves latently the clustering and discriminative information, indicating the model is more accurate for feature selection. Comparative studies validate the effectiveness of the proposed method.
(iii) The task of land cover classification for hyperspectral images is often confronted with the problem of a small number of labeled samples with high dimensionality. A typical existing methodology for this kind of problem is the technique of graph-based label propagation that is able to utilize the large number of unlabeled samples in semi-supervised learning. However, the spatial resolution of hypersepctral remote sensing images is usually low, and the land covers are usually spatially located in many small regions in the images. Technically, these small regions would be smoothed away during label propagation, resulting in a poor classification. To solve this drawback, we proposed a semi-supervised classification approach, which can automatically produce labeled samples. First, a classifier is trained with the small number of labeled samples, and the trained classifier is then employed to classify the hyperspectral images in pixel-level. Based on the pixel-level classification results, new seeds (namely pixels) are identified
and produced as newly-labeled ones under a criterion of label consistency in local regions. Accordingly, the label probabilities of these new seeds together with the previous ones offered in advance are propagated on a graph, which is constructed to preserve the local edges. Finally, the label propagation problem is addressed in terms of semi-supervised classification and formulated as an energy minimization problem, which can be solved via a sparse linear system. Comparative studies show that the proposed
method is superior to the existing methods on classification accuracy.
学科领域模式识别与智能系统
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/11852
专题毕业生_博士学位论文
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
李海昌. 高光谱遥感图像的特征选择和分类算法研究[D]. 北京. 中国科学院大学,2016.
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