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.     

基于矩阵分解的高光谱数据特征提取

利用有限的标记样本,将其作为硬性约束加入矩阵分解中;同时构建局部邻域graph,挖掘数据的流形结构并保持局部的不变特性,提出一种基于矩阵分解的高光谱数据特征提取(FEMF)方法.经过矩阵分解,使得原始高维光谱特征空间中相近的数据在低维空间中仍然相近,而相同类别的标记数据则被投影到同一个位置.这样的低维表示具有更强的判别性能,从而得到更好的分类和聚类效果.该方法的求解过程是非凸规划问题,同时给出了一个乘性更新规则获得局部优化解.最后,对真实高光谱数据进行特征提取验证了该方法的有效性.     

应用监督近邻重构分析的高光谱遥感数据特征提取

针对高光谱遥感数据特征提取方法的研究,提出了一种新的监督近邻重构分析（Supervised Neighbor Reconstruction Analysis,SNRA）算法。该方法首先利用同一类别的近邻数据点对各数据点进行重构;然后在低维嵌入空间中保持该重构关系不变,尽可能地分离开非同类数据点,并利用总体散度矩阵来约束数据间的相关性;最后求解得到一个最佳投影矩阵,进而提取出鉴别特征。SNRA算法不仅保持了同类数据的局部结构而且增强了非同类数据的可分性,同时减少了数据的冗余信息。在Indian Pine和KSC高光谱遥感数据集上的实验结果表明:提出的方法能更好地揭示出高光谱遥感数据的内在特性,提取出更有效的鉴别特征,改善分类效果。     

基于多尺度卷积神经网络的高光谱图像分类算法

为了提高高光谱图像的分类精度,提出了一种基于多尺度卷积神经网络的高光谱图像分类算法.首先,利用等距特征映射算法处理高光谱数据,以挖掘数据的非线性特性,保持数据点的内在几何性质;然后,构建以标记像元为中心的训练图像块,训练多尺度卷积神经网络;最后,利用softmax分类器预测测试像元的标签.提出的方法在Indian Pines、University of Pavia和Salinas scene高光谱遥感数据集上进行分类实验,并与CNN、R-PCA CNN、CNN-PPF、CD-CNN等算法进行性能比较.实验结果表明,在3个数据集上提出的方法的总体识别精度分别达到98.51％、98.64％和99.39％,与CNN算法相比分别提高了约8.35％、6.37％和7.81％.本文提出的方法无论是在分类精度还是Kappa系数上都优于另外4种方法,是一种较好的高光谱遥感数据分类方法.     

高光谱遥感图像分类算法的研究

现如今高光谱图像分类广泛应用于遥感图像的分析。高光谱图像像素级分类是利用高光谱图像的主要特点——丰富的光谱信息,对地面物体进行逐像素的高精度类别划分。通过对高光谱遥感图像独特的高光谱信息分析,从算法研究方面,对目前高光谱图像的像素级分类的研究进展和对今后的研究方主要从辅助方法、机器学习方法、深度学习方法三个方面总结高光谱图像分类领域的研究现状。未来高光谱分类算法的发展方向将更好的结合高光谱图像的特性,形成完整的深度学习系统。     

基于支持向量机的高光谱遥感图像分类

多数传统分类算法应用于高光谱分类都存在运算速度慢、精度比较低和难以收敛等问题.本文从支持向量机基本理论出发建立了一个基于支持向量机的高光谱分类器,并用国产OMIS传感器获得的北京中关村地区高光谱遥感数据进行试验,分析比较了各种SVM核函数进行高光谱分类的精度,以及网格搜寻的方法来确定C和愕闹?结果表明SVM进行高光谱分类时候径向基核函数的分类精度最高,是分类的首选.并且与神经网络径向基分类算法以及常用的最小距离分类算法进行比较,分类的精度远远高于SVM分类算法进行分类的结果.SVM方法在高光谱遥感分类领域能得到广泛的应用.     

基于流形正则化非负矩阵分解的高光谱数据降维

采用基于流形正则化非负矩阵分解(MR-NMF)的高 光谱数据降维方法。新方 法通过构建样本的近邻图描述数据几何结构,然后将其作为正则项加入NMF的目标函 数中进行组合优化。在真实的高光谱数据集HYDICE上进行的实验结果表明,新方法能 够提高高光谱图像分类的精度。     

基于支持向量机的高光谱遥感图像分类

分析和处理航空高光谱遥感图像对于我国遥感事业意义重大,文章首先分析了高光谱遥感图像的特征,依据该特征确定了支持向量机分类方法以及相应的参数优化确定的方法,并最终构建了高光谱遥感图像的分类模型。     

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.     

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。     

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.     

叶面降尘的高光谱定量遥感模型

利用自主设计的叶面降尘量测定方法,测定了榆树叶面的降尘量数据,结合地面高光谱遥感数据,研究了叶面降尘对榆树叶片高光谱特征的影响及叶面降尘量的高光谱定量反演.研究结果表明,叶面降尘可提高可见光波段的反射率,降低近红外波段的反射率,且对可见光波段的影响要大于近红外波段; 叶面降尘对三边位置没有影响,对三边幅值和面积有明显影响; 利用降尘光谱指数和三边参数建立的叶面降尘量模型,只具备粗略预测能力,而采用多元线性回归、主成分回归、偏最小二乘回归建立的模型,均具有很强的预测能力,其中以一阶微分建立的偏最小二乘回归模型的效果最佳,预测决定系数为0.92,预测均方根误差为1.06,样本标准差与预测均方根误差比为8.2.     

Cluster-space representation for hyperspectral data classification

This paper presents a generalization of the hybrid supervised-unsupervised approach to image classification, and an automatic procedure for implementing it with hyperspectral data. Cluster-space representation is introduced in which clustered training data is displayed in a one-dimensional (1-D) cluster-space showing its probability distribution. This representation leads to automatic association of spectral clusters with information classes and the development of a cluster-space classification (CSC). Pixel labeling is undertaken by a combined decision based on its membership of belonging to defined clusters and the clusters' membership of belonging to information classes. The method provides a means of class data separability inspection, visually and quantitatively, regardless of the number of spectral bands used. The class modeling requires only that first degree statistics be estimated; therefore, the number of training samples required can be many fewer than when using Gaussian maximum likelihood (GML) classification. Experiments are presented based on computer generated data and AVIRIS data. The advantages of the method are demonstrated showing improved capacity for data classification     

结合NAPCA 和复小波变换的高光谱遥感图像去噪

提出了一种能够良好地保持高光谱遥感图像细节特征的噪声去除方法。该方法首先利用噪声调整的主成分分析(NAPCA)进行特征提取,再利用复小波变换(CWT)对NAPCA 变换后的低能量成分进行去噪处理。对此低能量成分的每个波段利用二维复小波去噪,此时复小波系数采用BivaShrink 函数进行收缩。然后对低能量成分的每条光谱进行一维复小波变换,利用邻域阈值函数进行小波系数的收缩。对AVIRIS 图像贾斯珀桥、月亮湖和盆地进行的仿真实验表明:该方法去噪后的信噪比与HSSNR 相比提高了4.3~7.8 dB,与PCABS 相比提高了0.8~0.9 dB,验证了该算法的可行性。真实数据OMIS 图像的实验结果验证了该方法的有效性和适用性。     

高光谱遥感图像非线性解混研究综述

介绍了近年来非线性光谱解混方法的发展状况,主要包括矿物沙地地区的紧密混合模型和植被覆盖区域的多层次混合模型,以及基于这些模型的非线性解混算法和利用核函数、流形学习等方法的数据驱动非线性光谱解混算法及非线性探测算法.最后分析总结了现有非线性解混模型与算法的优势与缺陷及未来的研究趋势.     

Fusion of hyperspectral data using segmented PCT for color representation and classification

Fusion of hyperspectral data is proposed by means of partitioning the hyperspectral bands into subgroups, prior to principal components transformation (PCT). The first principal component of each subgroup is employed for image visualization. The proposed approach is general, with the number of bands in each subgroup being application dependent. Nevertheless, the paper focuses on partitions with three subgroups suitable for RGB representation. One of them employs matched-filtering based on the spectral characteristics of various materials and is very promising for classification purposes. The information content of the hyperspectral bands as well as the quality of the obtained RGB images are quantitatively assessed using measures such as the correlation coefficient, the entropy, and the maximum energy-minimum correlation index. The classification performance of the proposed partitioning approaches is tested using the K-means algorithm.     

A bayesian approach to classification of multiresolution remote sensing data

Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This work presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset. Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Results obtained on simulated and real satellite images are presented.