PCA数值算法在遥感图像处理中的应用

提出一种主分量分析(PCA)的数值算法并介绍了其在遥感图像处理中的应用,主要包括三方面内容:多光谱图像压缩;多光谱图像消噪;多光谱图像与spot全色图像的信息融合.软件实验验证和评价了该算法的有效性和准确性.     

视觉传达技术的多光谱图像高效压缩方法

多光谱图像包含了大量冗余信息，影响多光谱图像存储和传输，为了减少多光谱图像存储空间，提高多光谱图像传输速度，提出了基于视觉传达技术的多光谱图像高效压缩方法。首先采用多光谱图像，并采用视觉传达技术对多光谱图像进行去噪和增强处理，提高多光谱图像质量，然后运用卷积神经网络设计多光谱图像压缩模型，对光谱图像进行有效压缩，最后与经典方法进行了多光谱图像压缩对比实验，结果表明本方法能够实现多光谱图像高效压缩，压缩时间控制20 ms以内，而且多光谱图像压缩率超过40%,远远高于对比方法，同时可以保证压缩后多光谱图像质量，具有十分明显的优越性。     

基于小波变换和分段DPCM混合编码的多光谱遥感图像压缩算法

多光谱遥感图像的压缩要利用图像谱内及谱间的相关特性。该文在分析多光谱图像谱内和谱间相关特性的基础上,提出了对多光谱遥感图像进行压缩的分段 DPCM和 SPIHT相结合的混合压缩算法,即首先利用分段 DPCM算法去除谱间冗余,再利用高效的 SPIHT小波压缩算法对预测误差图像进行编码。实验取得了令人满意的效果,证明了该算法的有效性。     

采用区域互信息的多光谱与全色图像融合算法

为了提高多光谱与全色图像融合算法质量,提出了一种采用区域互信息的多光谱与全色图像融合算法。首先将多光谱图像变换至HSV彩色空间,并采用分水岭与区域合并的方法对V分量进行区域分割,得到区域分割映射,欧氏光谱距离作为区域合并的测度。然后采用非下采样Contourlet变换（Nonsubsample Contourlet Transform,NSCT）对多光谱图像V分量和全色图像进行多分辨率分解,将区域分割结果映射至全色图像,通过计算对应区域间的互信息对多分辨率分解系数进行融合,获得融合图像的分解系数,最后通过NSCT反变换实现融合图像重构。图像融合算法对比实验表明,文中融合算法在充分保留了多光谱图像光谱信息的同时,尽可能多地注入了全色图像的细节信息,有效提高了多光谱图像的边缘特征。     

基于Contourlet变换的稀疏成分分析的高光谱图像分类研究

本文介绍了稀疏成分高光谱遥感技术发展建立在多光谱遥感技术之上[1],兴分析和Contourlet变换相关的理论知识,研究了基于Contourlet变换的稀疏成分分析的高光谱图像分类方法——将高光谱图像分类问题转化为解决源信号提取的盲源分离问题。通过实验提取分类数据,计算分类精度。比较实验数据可知,基于Contourlet变换的稀疏成分分析的高光谱图像分类精度高,取得了较好的分类效果。     

综合有向纹理特征及其在多光谱图像融合中的应用

纹理特征是多光谱图像中除光谱特征以外的一类重要的图像特征。该文通过对有向纹理特征的分析提出了综合有向纹理特征的概念,建立了求解综合有向纹理特征的方法,并在此基础之上提出了基于图像冗余小波域的综合有向纹理特征重要中心系数算法。该算法将多光谱图像的光谱信息与形态信息进行了有机的结合,在保持源图像光谱特征的同时也考虑到了纹理特征对于融合效果的影响。对模拟及真实多光谱图像融合实验结果的主观视觉评价、客观定量分析说明该算法与现有的同类多光谱图像融合算法相比,能够更有效地融合源图像信息、更好地保持源图像纹理特征。     

基于光谱估计与多光谱技术的光学元件表面疵病检测

为实现精密光学元件表面疵病的高效测量和精确统计,提出了一种基于光谱估计和多光谱技术的光学元件表面疵病检测方法。该方法利用光谱估计提取白光图像中不同波长的单光谱疵病图像,并合成多光谱疵病图像,然后采用优化后的OTSU(Otsu Image Segmentation Algorithm)分别对单光谱与多光谱疵病图像进行分析。基于该方法搭建了光学元件表面疵病检测装置,获得了白光照明条件下光学元件表面疵病的图像。实验结果表明,与原始白光图像相比,合成多光谱图像的疵病检出数量提升了1.85倍,疵病检出面积最大增加了6.0倍,检测效率得到明显提高。根据光学元件表面疵病的特性选取不同波长组合来生成单光谱与多光谱图像,可更加高效精确地检测出传统检测技术不易检出的疵病信息。     

一种超高分辨率遥感图像融合新算法

该文针对超高分辨率的全色光图像和多光谱图像的融合,提出了一种基于对应分析的图像融合新算法。该算法在对多光谱数据进行对应分析的基础上,利用冗余小波变换提取出全色光图像的空间细节信息并将其融入到成分空间。实验分别采用IKONOS和QuickBird数据,融合结果的目视效果与客观评价表明,相比现有同类融合方法,该方法能够在提高空间分辨率的同时更好地保持光谱特性,有效地减少了色彩失真的现象。     

基于PROSAIL模型的高光谱遥感图像模拟研究

针对高光谱图像数据难以获取的情况,提出了一种基于PROSAIL辐射 传输模型的高光谱图像模拟方法。首先,对PROSAIL模型的输入参数进行了敏感性分析。以 此为基础,针对5个典型生化参数(叶片结构参数N、叶绿素含量Cab、叶片干物质含量Cm、类 胡萝卜素含量Car和叶面积指数LAI),基于支持向量机构建了一种基于多光谱数据的参数计算模型。其 次,根据得到的典型生化参数图,通过利用PROSAIL模型逐像元计算获得了高光谱模拟图像。 对HJ1A环境卫星的CCD1多光谱图像数据进行了实验,实现了高光谱图像数据模拟。在将等效计算后的模拟结果 与HJ1A CCD1多光谱图像数据进行对比后发现,两幅图像对应波段的 平均结构相似度都超过了95%;除了蓝光波段之外,全图的相对误差均小于10%。结果表明,本文方 法可以很好地实现基于多光谱数据的高光谱图像模拟。     

一种大视场TDICCD相机的多传感器图像配准方法

某大视场TDICCD相机采用多片TDICCD拼接,多通道输出全色和多光谱遥感图像,为了获得良好的融合和拼接效果,本文提出了一种基于双线性插值的空域互相关配准方法。首先,应用双线性插值算法对多光谱各谱段图像进行放大,得到和全色图像相同大小的多光谱图像。然后,采用空域互相关配准方法对多光谱各谱段图像和全色图像进行配准,并对有重叠像元的两通道图像进行拼接。实验结果表明,本文方法快速,抗噪性和鲁棒性较高,使大视场TDICCD相机多通道遥感图像配准取得了良好的效果。     

一种自适应模糊密度赋值的小波变换遥感图像融合算法

该文提出了一种基于模糊积分的多光谱图像和全色遥感图像融合算法。该算法首先由多光谱图像和全色图像的小波系数来获取模糊积分的信度函数,再依据小波系数的局部窗口内的方差自适应的构造模糊密度,然后由模糊积分计算融合后的小波系数,最后进行小波逆变换,得到融合图像。实验表明,采用新的基于模糊积分的融合方法,只要选择适当的模糊密度,就可以使得融合图像在提高空间细节质量的同时,相比其它融合算法能够具有更好的光谱质量。     

基于可变类FCM算法的多光谱遥感影像分割

为了自动确定多光谱遥感影像中地物目标类别数,该文提出一种基于可变类模糊C均值(Fuzzy C-Means, FCM)的多光谱遥感影像分割方法。首先定义像素与聚类的非相似性测度并据此构建目标函数,而后通过求解目标函数得到最优模糊隶属度和聚类中心。其次,研究模糊因子与影像地物目标类别数的关系,并通过定义划分熵(Partition Entropy, PE)指数优选模糊因子,选择PE指数值稳定收敛后所对应的最小模糊因子值为最优模糊因子,根据模糊因子与类别数的关系得到最优类别数,从而实现了影像的可变类分割。最后,利用提出算法分别对合成和真实多光谱遥感影像进行分割实验,实验结果表明,提出算法不仅能自动确定影像的最优类别数,还能获得较好的分割结果,为实现自动确定遥感影像中地物目标类别数提供新方法。     

Multispectral code excited linear prediction coding and itsapplication in magnetic resonance images

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.     

Classification of multispectral images based on a fuzzy-possibilistic neural network

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic C-means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fuzzy logic systems in neural network architecture. Instead of one state in a neuron for the conventional Hopfield nets, each neuron occupies 2 states called membership state and typicality state in the proposed FPHN. The proposed network not only solves the noise sensitivity fault of Fuzzy C-means (FCM) but also overcomes the simultaneous clustering problem of possibilistic C-means (PCM) strategy. In addition to the same characteristics as the FPCM algorithm, the simple features of this network are clear potential in optimal problem. The experimental results show that the proposed FPHN can obtain better solutions in the classification of multispectral images.     

Optimal transforms for multispectral and multilayer image coding

Multispectral images are composed of a series of images at differing optical wavelengths. Since these images can be quite large, they invite efficient source coding schemes for reducing storage and transmission requirements. Because multispectral images include a third (spectral) dimension with nonstationary behavior, these multilayer data sets require specialized coding techniques. The authors develop both a theory and specific methods for performing optimal transform coding of multispectral images. The theory is based on the assumption that a multispectral image may be modeled as a set of jointly stationary Gaussian random processes. Therefore, the methods may be applied to any multilayer data set which meets this assumption. Although the authors do not assume the autocorrelation has a separable form, they show that the optimal transform for coding has a partially separable structure. In particular, they prove that a coding scheme consisting of a frequency transform within each layer followed by a separate KL transform across the layers at each spatial frequency is asymptotically optimal as the block size becomes large. Two simplifications of this method are also shown to be asymptotically optimal if the data can be assumed to satisfy additional constraints. The proposed coding techniques are then implemented using subband filtering methods, and the various algorithms are tested on multispectral images to determine their relative performance characteristics.     

基于NSCT的多光谱和全色图像的融合

提出了一种基于无下采样Contourlet变换的多光谱和全色图像的融合方法.该方法在对多光谱影像进行IHS变换的基础上,对多光谱的I分量和高分辨率的全色影像分别进行无下采样Contourlet变换(NSCT),然后对分解得到的近似分量以及各层金字塔各方向的细节分量利用本文提出的一定的融合准则分别对近似分量和细节分量进行影像融合,最后通过无下采样Conlourlet逆变换得到新的I分量,与H,S分量一起还原到RGB空间,最终得到融合后的高分辨率多光谱彩色图像.本文采用一组多光谱图像和全色图像数据进行融合实验,其实验融合图像的目视效果和统计指标均优于传统的IHS融合方法、小波融合方法以及Contourlet变换方法.     

Adaptive fuzzy segmentation of magnetic resonance images

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.     

多视场星敏感器近地轨道自主定位导航方法

传统航天器自主天文导航需要星敏感器、红外地平仪、磁强计等多种敏感器采集导航数据,增加了航天器的成本和复杂度。利用多视场星敏感器的特点,分别对恒星与地球进行成像,在完成姿态测量的同时,得到地心矢量信息,从而进行自主天文导航。首先建立地球几何模型,结合航天器轨道参数与多视场星敏感器的安装布局,实现各个视场内地球边缘的成像模拟,使用Steger 算法提取地球边缘。综合考虑地球扁率的影响,对不同视场中观测到的地球边缘进行拟合得到精确地心矢量,最后进行基于星光角距的直接敏感地平导航仿真。仿真结果表明,在一个视场观测恒星,另外两个视场观测地球边缘的布局情况下,地心矢量精度和导航位置精度分别达到0.017 2（1）和190 m（1）。     

基于神经网络的典型地物的偏振多光谱图像分类（特邀）

相比传统的多光谱成像探测,偏振多光谱成像探测方法可以探测目标表面的粗糙度、含水量等更多信息,给目标检测带来了很大便利,但目前主要用于目标探测,尚未广泛应用于目标分类。BP神经网络是目前常用的一种典型神经网络,可以建立从端到端的映射,在训练样本集足够大的前提下,训练完毕且效果良好的神经网络是一种高效、精确、快速的工具。首先,利用基于旋转偏振片和滤波片的偏振光谱成像探测系统获取了典型地物的偏振多光谱图像,对图像进行了预处理,建立了数据集;其次,在该数据集上进行了神经网络的训练,训练后的神经网络可以处理未知的偏振多光谱图像,并实现了对几种典型地物的分类;最后,对神经网络分类的效果进行了评价,并与其他几种典型分类方法的效果进行了对比,发现神经网络方法具有更好的分类精度和效果,相比典型的最大似然分类算法,其总体分类精度可从91.7%提升至94.2%,Kappa系数可从0.851提升至0.898。研究结果表明:基于神经网络的偏振光谱图像分类方法对于改进和优化现有的偏振多光谱图像数据处理方法具有一定的研究意义。     

Fuzzy two-step filter for impulse noise reduction from color images.

A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image.