基于图像欧氏距离的高光谱图像流形降维算法

提出两种基于图像欧氏距离的非线性降维方法.该方法利用高光谱图像物理特性, 将图像欧氏距离引入到传统的流形降维算法中.与其它应用于高光谱图像的降维算法相比, 该算法具有诸多优点.图像欧氏距离的引入, 在考虑高光谱图像本身的空间关系的同时, 很好地保持了数据点之间的局部特性, 可以实现有效地去除原始数据集光谱维和空间维的冗余信息.实际高光谱数据的实验结果表明, 该算法应用于高光谱图像分类时, 与其它常见的方法相比具有更高的分类精度.     

采用流形近邻传播聚类的极化SAR图像分类

针对传统近邻传播（Affinity Propagation, AP）聚类算法使用欧式距离构建相似度矩阵,不能有效描述极化SAR数据复杂分布的问题,本文提出一种新的基于联合流形距离的AP聚类算法(CMD-AP) 用于极化SAR图像分类。首先将待分类极化SAR图像分割成若干超像素,在相应的极化特征基础上加入图像纹理特征,利用拉普拉斯特征映射算法对特征降维;然后结合相干矩阵Wishart流形和特征矢量欧式流形作为流形距离测度,构造相似性矩阵;最后利用上述相似性矩阵,采用AP聚类算法,对极化SAR图像进行分类。该算法充分考虑了极化SAR数据集潜在的流形结构,将联合的流形距离测度引入AP算法中。实验表明,本文算法提高了极化SAR图像的分类精度,具有更优的区域一致性和边缘保持效果。      

基于光谱梯度角的高光谱影像流形学习降维法

为了挖掘高光谱数据的光谱局部特征,从高光谱遥感数据内在的非线性结构出发,提出了一种基于光谱梯度角的高光谱影像流形学习降维方法。采用局部化流形学习算法局部保持投影（LPP）对高光谱遥感数据进行非线性降维,对距离度量进行改进,将能够更好刻画高光谱影像光谱局部特征的光谱梯度角相似性度量应用于LPP方法,并用真实高光谱图像进行降维实验,取得了优于LPP方法和采用光谱角的LPP方法的结果。结果表明,在光谱规范化特征值方面,所提方法优于LPP方法和采用光谱角的LPP方法;在信息量的保持方面,具有更好的局部细节信息保持量。采用光谱梯度角的流形学习方法用于高光谱影像降维能取得较好的降维效果。     

基于图像欧式距离和拉普拉斯特征映射的端元提取算法

由于多重反射和散射,高光谱图像中的混合像元实际上是非线性光谱混合。传统的端元提取算法是以线性光谱混合模型为基础,因此提取精度不高。针对高光谱图像的非线性结构,提出了基于图像欧氏距离非线性降维的高光谱遥感图像端元提取方法。该方法结合高光谱数据的物理特性,将图像欧氏距离引入拉普拉斯特征映射进行非线性降维以更好地去除高光谱数据集中冗余的空间信息和光谱维度信息,然后对降维后的数据利用寻找最大单形体体积的方法提取端元。真实高光谱数据实验表明,提出的方法对高光谱图像端元提取具有良好的效果,性能优于线性降维的主成份分析算法和原始的拉普拉斯特征映射算法。     

基于流形波段选择的高光谱图像分类

为解决高光谱图像中高维数据和有标记训练样本不 足的矛盾导致“维度灾难”问题,提出一种无监督的基于流形学习的波段选择(MLBS)方法。 首先通过流形学习方法,得到原始数据的流形嵌入映射;然后通过LASSO优化过程,运用顺 向坐标下降算法,得到原始波段对每个流形结构 维度的贡献度;最后统计每个波段的贡献度,选取贡献度大的波段形成波段子集。用 真实的AVIRIS高光谱图像对算法进行仿真实验的结果表明,本文方法在小样本下的高光谱 地物分类识别问题上具有良好的效果。     

基于低秩张量分析的高光谱图像降维与分类

提出一种用于高光谱图像降维和分类的分块低秩张量分析方法。该算法以提高分类精度为目标,对图像张量分块进行降维和分类。将高光谱图像分成若干子张量,不仅保存了高光谱图像的三维数据结构,利用了空间与光谱维度的关联性,还充分挖掘了图像局部的空间相关性。与现有的张量分析法相比,这种分块处理方法克服了图像的整体空间相关性较弱以及子空间维度的设定对降维效果的负面影响。只要子空间维度小于子张量维度,所提议的分块算法就能取得较好的降维效果,其分类精度远远高于不分块的算法,从而无需借助原本就不可靠的子空间维度估计法。仿真和真实数据的实验结果表明,所提议分块低秩张量分析算法明显地表现出较好的降维效果,具有较高的分类精度。     

基于图像分割和LSSVM的高光谱图像分类

提出一种基于图像分割和LSSVM的高光谱图像分类方法,将空谱信息结合起来进行高光谱图像的分类。首先利用均值漂移算法对高光谱图像进行分割,然后对每一块分割区域数据进行降维并且对降维后的数据LSSVM分类,最后用最大投票方法融合分割图和分类得到最终的分类结果。该文分类方法先对分割后的区域求出相似性矩阵并训练新样本集求出低秩系数矩阵,由相似性矩阵和低秩系数矩阵构造特征值方程求解出降维矩阵,然后利用混合核LSSVM对降维后的数据进行分类。实验结果表明,提出的基于图像分割和LSSVM的高光谱图像分类方法有效提高了高光谱图像的分类精度。     

空谱结构保持的高光谱图像分类

高光谱遥感图像具有特征（波段）数多、冗余度高等特点,因此特征选择成为高光谱分类的研究热点。针对此问题,提出了空间结构与光谱结构同时保持的高光谱数据分类算法。考虑高光谱图像的物理特性,首先对图像进行加权空谱重构,使图像的空间结构信息自动融入光谱特征,形成空谱特征集;对利用最小二乘回归模型保存数据集的全局相似性结构的基础上,加入局部流形结构正则项,使挑选的特征子集更好地保存数据集的内在本质结构;讨论了窗口大小和正则参数对分类精度的影响。对Indian Pines、PaviaU和Salinas数据集的实验表明,该算法得到的特征子集的总体分类精度达到93.22%、96.01%和95.90%。该算法不仅充分利用了高光谱图像的空间结构信息,而且深入挖掘了数据集的内在本质结构,从而得到更有鉴别性的特征子集,相比传统方法明显提高了分类精度。     

3维卷积递归神经网络的高光谱图像分类方法

为了针对高光谱图像中空间信息与光谱信息的不同特性进行特征提取，提出一种3维卷积递归神经网络(3-D-CRNN)的高光谱图像分类方法。首先采用3维卷积神经网络提取目标像元的局部空间特征信息，然后利用双向循环神经网络对融合了局部空间信息的光谱数据进行训练，提取空谱联合特征，最后使用Softmax损失函数训练分类器实现分类。3-D-CRNN模型无需对高光谱图像进行复杂的预处理和后处理，可以实现端到端的训练，并且能够充分提取空间与光谱数据中的语义信息。结果表明，与其它基于深度学习的分类方法相比，本文中的方法在Pavia University与Indian Pines数据集上分别取得了99.94%和98.81%的总体分类精度，有效地提高了高光谱图像的分类精度与分类效果。该方法对高光谱图像的特征提取具有一定的启发意义。     

面向高光谱遥感影像分类的监督多流形鉴别嵌入方法

流形学习方法可以发现嵌入于高维观测数据中的低维流形结构,但是传统的流形学习算法都是假设所有数据位于单一流形上,忽略了高维数据中不同的子集可能存在不同的流形.针对上述问题,本文提出一种监督多流形鉴别嵌入的维数约简方法,并应用于高光谱遥感影像分类.该方法首先利用样本数据的类别标签进行多子流形划分,在此基础上采用图嵌入理论构造流形内图和流形间图,然后通过最小化流形内距离同时最大化流形间距离以增强类内数据聚集性和类间数据分散性,提取低维鉴别特征,改善地物分类性能.在University of Pavia （PaviaU）和Kennedy Space Center （KSC）高光谱数据集上的实验表明,相较于其他单流形算法和多流形算法,该方法取得了更高的分类精度,在随机选取2%训练样本时,其总体分类精度分别达到88.04%和84.53%,有效提升了地物分类性能.     

Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications

Hyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For increasing the capabilities of the data in terms of utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by combining the hyperspectral image with a high spatial resolution panchromatic image. These techniques are generally known as pansharpening and can be divided into component substitution (CS) and multi-resolution analysis (MRA) based methods. In general, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. Both substitution and filtering approaches are considered adequate when applied to multispectral and PAN images, but have many drawbacks when the low-resolution image is a hyperspectral image. Thus, one of the main challenges in hyperspectral pansharpening is to improve the spatial resolution while preserving as much as possible of the original spectral information. An effective solution to these problems has been found in the use of hybrid approaches, combining the better spatial information of CS and the more accurate spectral information of MRA techniques. In general, in a hybrid approach a CS technique is used to project the original data into a low dimensionality space. Thus, the PAN image is fused with one or more features by means of MRA approach. Finally the inverse projection is used to obtain the enhanced image in the original data space. These methods, permit to effectively enhance the spatial resolution of the hyperspectral image without relevant spectral distortions and on the same time to reduce the computational load of the entire process. In particular, in this paper we focus our attention on the use of Nonlinear Principal Component Analysis (NLPCA) for the projection of the image into a low dimensionality feature space. However, if on one hand the NLPCA has been proved to better represent the intrinsic information of hyperspectral images in the feature space, on the other hand an analysis of the impact of different fusion techniques applied to the nonlinear principal components in order to define the optimal framework for the hybrid pansharpening has not been carried out yet. More in particular, in this paper we analyze the overall impact of several widely used MRA pansharpening algorithms applied in the nonlinear feature space. The results obtained on both synthetic and real data demonstrate that an accurate selection of the pansharpening method can lead to an effective improvement of the enhanced hyperspectral image in terms of spectral quality and spatial consistency, as well as a strong reduction in the computational time.     

基于谱间结构相似先验的高光谱压缩感知重构

在高光谱压缩感知重构中,充分利用图像的先验信息能有效提升算法的重构精度。现有重构算法均未考虑高光谱图像的谱间结构冗余信息,该文提出一种基于谱间结构相似先验的高光谱压缩感知重构方法。该方法通过谱间结构冗余定义高光谱结构图像,以结构图像为基础,设计一个压缩感知重构正则项,再结合高光谱图像的空间相关性和谱间统计相关性,提出一种新的压缩感知高光谱图像联合重构方案,并设计一种基于变量拆分的有效的求解算法。实验表明,在相同观测值数目下,该文算法的重构质量明显优于现有算法。     

Spatial/spectral endmember extraction by multidimensional morphological operations

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.     

一种考虑光谱变异性的高光谱图像非线性解混算法

非线性解混可以解释高光谱图像复杂场景中的非线性混合效应,但地物的光谱变异性是其中的一个难点。提出一种考虑光谱变异性的无监督非线性解混算法。通过核函数将原始高光谱图像数据隐式地映射到高维特征空间中,从而在该空间中结合光谱变异性进行线性解混;与此同时,依据实际地物的分布特性,添加丰度和光谱变异系数的局部平滑约束。模拟和真实高光谱数据的实验结果表明,该方法能克服不同非线性混合场景中存在的光谱变异性问题,提高光谱解混的精度。     

Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

This work describes sequences of extended morphological transformations for filtering and classification of high-dimensional remotely sensed hyperspectral datasets. The proposed approaches are based on the generalization of concepts from mathematical morphology theory to multichannel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Extended morphological transformations, characterized by simultaneously considering the spatial and spectral information contained in hyperspectral datasets, are applied to agricultural and urban classification problems where efficacy in discriminating between subtly different ground covers is required. The methods are tested using real hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer and the German Aerospace Agency Digital Airborne Imaging Spectrometer (DAIS 7915). Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately.     

Dimensionality reduction for hyperspectral imagery based on fastica

The high dimensions of hyperspectral imagery have caused burden for further processing. A new Fast Independent Component Analysis (FastICA) approach to dimensionality reduction for hyperspectral imagery is presented. The virtual dimensionality is introduced to determine the number of dimensions needed to be preserved. Since there is no prioritization among independent components generated by the FastICA, the mixing matrix of FastICA is initialized by endmembers, which were extracted by using unsu-pervised maximum distance method. Minimum Noise Fraction (MNF) is used for preprocessing of original data, which can reduce the computational complexity of FastICA significantly. Finally, FastICA is performed on the selected principal components acquired by MNF to generate the expected independent components in accordance with the order of endmembers. Experimental results demonstrate that the proposed method outperforms second-order statistics-based transforms such as principle components analysis.     

A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantially different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied.     

面向目标检测基于稀疏表示的波段选择方法

随着高光谱成像技术的发展,日益提高的光谱分辨率在提高目标检测和识别能力的同时,其较高的数据维度和较大的数据量也为数据分析和处理带来了很大的挑战.波段选择作为一种有效提高处理效率的技术受到广泛关注,但却鲜有专门针对目标检测设计的方法.针对上述问题,本文在分析约束能量最小化（CEM）检测算法特点的基础上,提出了一种面向目标检测,基于稀疏表示的波段选择方法.该方法首先基于数据的对称KL散度分布情况,将原始高光谱数据划分为若干波段子空间.然后在各子空间内稀疏重构检测结果,利用选择波段与稀疏向量非零项的一一对应关系,通过求解最优化问题实现波段选择.实验结果验证了该方法的有效性.