多源遥感图像配准技术综述

从成像光谱特性、成像分辨率和成像模式等方面对可见光、红外、高光谱和合成孔径雷达传感器的成像特点进行分析,根据一致性特征描述方法对多源遥感图像配准算法进行分类,指出多源遥感图像具有成像特性变化大、相关度小、匹配特征的空间分布不均匀等特点,其配准技术的关键在于提取不变的图像特征以及得到有效的匹配特征。     

深度学习在高光谱图像分类领域的研究现状与展望

高光谱图像（Hyperspectral imagery,HSI）分类是高光谱遥感对地观测技术的一项重要内容,在军事及民用领域都有着重要的应用.然而,高光谱图像的高维特性、波段间高度相关性、光谱混合等使得高光谱图像分类面临巨大挑战.近年来,随着深度学习新技术的出现,基于深度学习的高光谱图像分类在方法和性能上得到了突破性的进展,为其研究提供了新的契机.本文首先介绍了高光谱图像分类的背景、研究现状及几个常用的数据集,并简要概述了几种典型的深度学习模型,最后详细介绍了当前的一些基于深度学习的高光谱图像分类方法,总结了深度学习在高光谱图像分类领域中的主要作用和存在的问题,并对未来的研究方向进行了展望.     

基于主动轮廓模型的玉米种子高光谱图像分类

提出将主动轮廓模型(Active contour model,ACM)应用于玉米种子的高光谱图像分割中.首先,通过高光谱成像系统获取9个品种共432粒玉米种子的高光谱反射图像,利用基于主动轮廓模型的图像分割法对玉米种子高光谱图像提取目标区域轮廓,得到单波段下每粒玉米种子12个形状特征参数,然后通过主成分分析法(Principal component analysis,PCA)对特征数据降维,结合波段间的相关性选出12个最优波段,最后利用误差反向传播(Back propagation,BP)神经网络模型进行建模分类,与传统的阈值分割法相比,取得了更好的分类效果.研究结果为高光谱图像目标轮廓提取提供了一种新方法.     

高光谱遥感图像分类算法中的应用研究

针对高光谱遥感图像数据量大、维数高、数据之间冗余量大的特点,提出一种基于决策边界特征提取(Decision Bounda-ry Feature Extraction,DBFE)的SVM高光谱遥感图像分类算法。首先采用DBFE对高光谱遥感图像进行特征提取,消除特征之间相关性,并降低特征维数,然后采用GA对SVM参数进行优化,找到最优分类模型参数,最后采用最优分类模型对待分类的高光谱遥感图像进行分类。仿真结果表明,高光谱遥感图像分类算法提高了高光谱遥感图像分类的效率和分类正确率,说明分类方法是有效、可行的。     

基于类别可分性的高光谱图像波段选择

高光谱遥感数据具有丰富的光谱信息,应用十分广泛,但其冗余的光谱信息有时会限制高光谱图像的分类等的精度以及计算复杂度.为了提高解译效率,高光谱图像降维不可或缺,这也是高光谱图像处理的研究热点之一.提出了一种基于类别可分性的高光谱图像波段选择方法(Endmember Separability Based band Selection,ESBB),该方法通过Mahalanobis距离最大化图像中各类地物的可分性来确定最优的波段组合.相较于其他监督波段选择算法,该方法不需要大量训练样本,不用对每个组合做分类处理.对波段选择后的结果进行分类的实验结果证明,该方法是一个快速有效的波段选择方法,可以得到一个较好的分类精度.     

基于改进GraphSAGE的高光谱图像分类

针对传统的图卷积网络节点嵌入过程中接受邻域范围小的问题, 本文提出了一种基于改进GraphSAGE算法的高光谱图像分类网络. 首先, 利用超像素分割算法对原始图像进行预处理, 减少图节点的个数, 既最大化保留了原始图像的局部拓扑结构信息, 又降低了算法的复杂度, 缩短运算时间; 其次, 采用改进的GraphSAGE算法, 对目标节点进行平均采样, 选用平均聚合函数对邻居节点进行聚合, 降低空间复杂度. 在公开的高光谱图像数据集Pavia University和Kenndy Space Center上与相关模型进行对比, 实验证明, 基于改进GraphSAGE算法的高光谱图像分类网络可以取得较好的分类结果.     

综合纹理特征的高光谱遥感图像分类方法

提出了一种基于Gabor滤波的高光谱遥感图像支持向量机(SVM)分类方法,通过将Gabor滤波器组产生的纹理特征引入SVM分类,不仅充分利用了SVM适于解决高维数据分类问题的优势,而且在分类过程中实现了空间结构信息和光谱信息的综合使用,有效利用了高光谱图像“图谱合一”的特性.采用中科院上海技术物理研究所研制的模块化成像光谱仪OMIS (operative modular imaging spectrometry)真实数据进行的实验,实验结果表明,该方法提高了分类效果,分类结果更具有空间连贯性,并且能有效地克服噪声的影响.     

基于近邻协同的高光谱图像谱-空联合分类

遥感高光谱成像能够获得丰富的地物光谱信息, 为高精度的地物分析提供了可能. 针对高光谱图像分类中通常面临的数据维数高、标记样本少、计算量大等问题, 提出了一种简单有效的谱--空联合分类方法. 利用高光谱图像丰富的光谱信息和地物分布的空间平滑特性, 该算法首先对光谱数据进行特征提取和空间滤波, 然后利用本文提出的基于近邻协同的支持向量机(Neighborhood collaborative support vector machine, NC-SVM)进行分类. 近邻协同进一步利用地物分布的空间平滑特性, 通过联合空间近邻的判决信息进行中心像素的类别判定, 有效减小了只有少量训练样本下的错分概率. 实验表明, 相比已有的相关方法, 该算法在不明显增加计算量的情况下可获得更高的分类正确率, 能够实现少量训练样本下高光谱图像的快速高精度分类.     

高光谱图像的无损压缩研究进展

随着成像光谱仪的普及应用,遥感图像的空间分辨率、谱间分辨率、时间分辨率越来越高,使得成像光谱数据量迅速增长,对海量数据进行有效的压缩成了遥感技术发展中迫切需要解决的一个问题.由于有损压缩可能会丢掉对进一步处理非常有用的信息,通常采用无损压缩方法.本文首先介绍了高光谱图像的特点和无损压缩的基本原理,然后综述了高光谱图像无损压缩的研究进展,最后展望了研究前景.     

近红外高光谱图像数据预测技术

目的 受到传感器光谱响应范围的影响,可见光区域和近红外区域（400~2 500 nm）的高光谱数据通常使用不同的感光芯片进行成像,现有这一光谱区域典型的高光谱成像系统,如AVIRIS （airborne visible infrared imaging spectrometer）成像光谱仪,通常由多组感光芯片组成,整个成像系统成本和体积通常比较大,严重限制了该谱段高光谱探测技术的发展。为了能够扩展单感光芯片成像系统获得的高光谱图像的光谱范围,本文探索基于卷积神经网络的近红外光谱数据预测技术。方法 结合AVIRIS成像光谱仪的光谱配置,设计了基于残差学习的红外谱段图像预测网络,利用计算成像的方式从可见光范围的高光谱图像预测出近红外波段的光谱图像,并在典型的卫星高光谱遥感数据上进行红外光谱预测重构和基于重构的数据分类实验,以验证论文提出的红外光谱数据预测技术的可行性以及有效性。结果 本文设计的预测网络在Cuprite数据集上得到的预测近红外图像峰值信噪比为40.145 dB,结构相似度为0.996,光谱角为0.777 rad;在Salinas数据集上得到的预测近红外图像峰值信噪比为39.55 dB,结构相似性为0.997,光谱角为1.78 rad。在分类实验中,相比于只使用可见光图像,利用预测的近红外图像使得支持向量机（support vector machine,SVM）的准确率提升了0.6%,LeNet的准确率提升了1.1%。结论 基于AVIRIS传感器获取的两组典型卫星高光谱数据实验表明,本文提出的红外光谱数据预测技术不仅可基于计算成像的方式扩展可见光光谱成像系统的光谱成像范围,对于减小成像系统体积和质量具有重要意义,而且可有效提高可见光区域光谱图像数据在典型应用中的处理性能,对于提高高光谱数据处理精度提供新的技术支撑。     

DepthLimited crossover in GP for classifier evolution

Genetic Programming (GP) provides a novel way of classification with key features like transparency, flexibility and versatility. Presence of these properties makes GP a powerful tool for classifier evolution. However, GP suffers from code bloat, which is highly undesirable in case of classifier evolution. In this paper, we have proposed an operator named “DepthLimited crossover”. The proposed crossover does not let trees increase in complexity while maintaining diversity and efficient search during evolution. We have compared performance of traditional GP with DepthLimited crossover GP, on data classification problems and found that DepthLimited crossover technique provides compatible results without expanding the search space beyond initial limits. The proposed technique is found efficient in terms of classification accuracy, reduced complexity of population and simplicity of evolved classifiers.     

Two-Tier genetic programming: towards raw pixel-based image classification

Classifying images is of great importance in machine vision and image analysis applications such as object recognition and face detection. Conventional methods build classifiers based on certain types of image features instead of raw pixels because the dimensionality of raw inputs is often too large. Determining an optimal set of features for a particular task is usually the focus of conventional image classification methods. In this study we propose a Genetic Programming (GP) method by which raw images can be directly fed as the classification inputs. It is named as Two-Tier GP as every classifier evolved by it has two tiers, the other for computing features based on raw pixel input, one for making decisions. Relevant features are expected to be self-constructed by GP along the evolutionary process. This method is compared with feature based image classification by GP and another GP method which also aims to automatically extract image features. Four different classification tasks are used in the comparison, and the results show that the highest accuracies are achieved by Two-Tier GP. Further analysis on the evolved solutions reveals that there are genuine features formulated by the evolved solutions which can classify target images accurately.     

Evolutionary constructive induction

Feature construction in classification is a preprocessing step in which one or more new attributes are constructed from the original attribute set, the object being to construct features that are more predictive than the original feature set. Genetic programming allows the construction of nonlinear combinations of the original features. We present a comprehensive analysis of genetic programming (GP) used for feature construction, in which four different fitness functions are used by the GP and four different classification techniques are subsequently used to build the classifier. Comparisons are made of the error rates and the size and complexity of the resulting trees. We also compare the overall performance of GP in feature construction with that of GP used directly to evolve a decision tree classifier, with the former proving to be a more effective use of the evolutionary paradigm.     

Application of genetic programming for multicategory patternclassification

Explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem. GP can discover relationships and express them mathematically. GP-based techniques have an advantage over statistical methods because they are distribution-free, i.e., no prior knowledge is needed about the statistical distribution of the data. GP also automatically discovers the discriminant features for a class. GP has been applied for two-category classification. A methodology for GP-based n-class classification is developed. The problem is modeled as n two-class problems, and a genetic programming classifier expression (GPCE) is evolved as a discriminant function for each class. The GPCE is trained to recognize samples belonging to its own class and reject others. A strength of association (SA) measure is computed for each GPCE to indicate the degree to which it can recognize samples of its own class. SA is used for uniquely assigning a class to an input feature vector. Heuristic rules are used to prevent a GPCE with a higher SA from swamping one with a lower SA. Experimental results are presented to demonstrate the applicability of GP for multicategory classification, and they are found to be satisfactory. We also discuss the various issues that arise in our approach to GP-based classification, such as the creation of training sets, the role of incremental learning, and the choice of function set in the evolution of GPCE, as well as conflict resolution for uniquely assigning a class     

基于遗传规划的多光谱图像分类

针对多光谱图像分类这一多类别模式识别问题,将二进制纠错编码与GP(GeneticProgramming)算法相结合,并用改进后的编码矩阵代替原先的二进制编码矩阵对图像进行分类,从而建立了新的基于GP的多光谱图像分类算法,给出了用该方法对多光谱图像中地物进行分类的实例。结果表明与以往基于GP的分类方法相比,该文方法体现出较高的分类性能,为遗传规划在多类别模式识别问题中的应用提供了又一条可行的途径。     

支持向量机的进化多核设计

为提高支持向量机分类精度,提出一种基于遗传程序设计的进化多核算法.算法中每个个体表示一个多核函数,并采用树形结构进行编码,增强了多核函数的非线性;初始种群由生长法产生,经过遗传操作后得到适合具体问题的进化多核函数.遗传程序设计的全局搜索性能使得算法设计不需要先验知识.与单核函数及其他多核函数的对比实验结果表明,进化多核...     

A comparison of linear genetic programming and neural networks inmedical data mining

We introduce a new form of linear genetic programming (GP). Two methods of acceleration of our GP approach are discussed: 1) an efficient algorithm that eliminates intron code and 2) a demetic approach to virtually parallelize the system on a single processor. Acceleration of runtime is especially important when operating with complex data sets, because they are occurring in real-world applications. We compare GP performance on medical classification problems from a benchmark database with results obtained by neural networks. Our results show that GP performs comparably in classification and generalization     

Breast cancer diagnosis using genetic programming generated feature

This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. A modified Fisher criterion is developed to help GP optimize features that allow pattern vectors belonging to different categories to distribute compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and automatically discover the relationship among data, to improve classification accuracy.     

Evolution of superFeatures through genetic programming

Abstract: The success of automatic classification is intricately linked with an effective feature selection. Previous studies on the use of genetic programming (GP) to solve classification problems have highlighted its benefits, principally its inherent feature selection (a process that is often performed independent of a learning method). In this paper, the problem of classification is recast as a feature generation problem, where GP is used to evolve programs that allow non‐linear combination of features to create superFeatures, from which classification tasks can be achieved fairly easily. In order to generate superFeatures robustly, the binary string fitness characterization along with the comparative partner selection strategy is introduced with the aim of promoting optimal convergence. The techniques introduced are applied to two illustrative problems first and then to the real‐world problem of audio source classification, with competitive results.