实测端元光谱和多光谱图像之间的模拟与细分

地物光谱特性是遥感应用的基础。本文以渭干河-库车河三角洲绿洲为研究区,首先选取裸土、植被两类地物作为研究对象,通过TM传感器的光谱响应函数,实现了将野外实测端元光谱拟合为多光谱离散光谱。其次在对TM图像的光谱波段进行细分的基础上,利用光谱知识库的数据支持来模拟获取具有更高光谱分辨率的细分光谱光学遥感图像,深入开展两种尺度相互转换的研究。结果表明:一、拟和的多光谱与TM像元光谱具有很好的相关性,在此基础上,采用线性算法建立端元光谱与遥感图像像元光谱的转换模型,实现了从实测端元光谱尺度向遥感多光谱像元尺度的定量光谱转换,为遥感定量分析奠定了一定基础。二、细分光谱模拟图像的方法能够较为可靠的模拟出真实高光谱分辨率图像的信息,模拟方法可信,达到了推广和验证的效果。     

空间分辨率之比对遥感图像融合质量的影响

本文主要探讨光学遥感图像融合中空间分辨率之比对融合质量的影响.采用IKONOS-2全色与多光谱图像,通过重采样的方法模拟空间分辨率之比连续变化的融合输入数据,并进行Gram-Schmidt融合实验,补充已有成果中空间分辨率之比变化不连续、融合方法单一的现状.结果表明:当空间分辨率之比降低时,融合质量随着下降,实际应用中,多光谱图像的空间分辨率越高越好;当空间分辨率之比很小时,应适当降低全色图像的空间分辨率,以减弱融合图像的光谱变形,提高融合质量;此外,即使空间分辨率之比很小,融合后图像也比融合输入多光谱图像的清晰度高,更利于图像判断与后续处理.     

遥感图像多光谱假彩色合成处理及其在环境地学中的应用

本文首先论述了遥感图像多光谱假彩色合成中组合波段的选择方法以及彩色合成的几种方法。通过分析比较，N维数据熵值原理选择法和HLC合成法的结合是一种有效的假彩色合成手段，并在计算机上实现了这种合成方法，最后，论及了多光谱假彩色合成遥感图像在环境地学中的一些应用。     

SAR图像与多光谱图像融合方法研究

本文针对SAR图像分辨率高、纹理信息多、斑噪影响大，而多光谱图像光谱信息丰富、分辨率较小的特点，提出了一种SAK与多光谱图像的数据融合算法。该方法分三步进行：首先使用粗糙集理论选取图像特征，在此基础上，对特征提取后的图像进行配准，配准中采用了数理统计中近似正态分布的方法，配准精度在一个像素以内，最后采用相关性加权的方法对配准后的SAK图像与多光谱图像进行融合。融合实验以LandSat的TM图像与Radsat的SAK图像为例，实验结果表明该方法取得了较好的融合效果。     

分辨率差异较大的遥感图像自动配准算法研究

本文在研究了已有配准算法的基础上,针对两个分辨率差异较大的遥感图像,提出了自动配准的改进方法,自动获取基准影像,并对每个波段的图像做直方图均衡化增强,最后,构建影像金字塔,运用SIFT算法提取特征点。     

光学遥感图像飞机目标识别算法

光学遥感图像中蕴含着大量信息,更新速度非常快。使用人工方法对光学遥感图像进行判读和目标的识别显然早已达不到现代社会各领域的需求。实时、高效地从光学遥感图像中识别出感兴趣目标具有非常重要的意义。本文对基于人工智能的图像飞机识别研究现状进行了总结,方便后续学者研究。     

小波双三次高倍插值搜索算法的遥感图像重建

由于光学遥感成像的距离较远和CCD物理尺寸的限制,获得的图像分辨率都比较低。为了在保持原始图像信息的情况下,提高图像的空间分辨率,改善图像的峰值信噪比,在深入分析前期研究中提出的小波双三次插值搜索算法的基础上,建立了小波双三次高倍插值搜索算法。该算法可以根据需要重建出原始遥感图像的高倍高分辨率遥感图像。实验结果表明,新算法重建出原始图像的2×2倍高分辨率图像的细节信息和视觉效果最好,3×3倍和4×4倍次之,5×5倍较差,更高倍数的高分辨率重建图像不宜在实际应用中采用。     

高速窄带多光谱成像系统光谱重建技术研究

光谱成像技术可以同时从光谱维和空间维上获取被测目标的信息，即结合了空间成像系统和光谱检测系统的功能，因此近年在影像获取与处理领域中倍受重视。本论文基于窄带多光谱成像技术建立八通道CCD多光谱成像系统，它能够实时采集八个通道的图像，获得波长分布从可见到红外（420-940nm）八个波段的光谱响应值。在此基础上对图像进行位置配准、反射率定标、采用插值算法获得其它波段光谱响应值，最终能够获取图像中任意一点的光谱反射率及颜色参数。实验结果表明，本文使用的三次样条插值法对原始光谱图像进行平滑操作的方法是有效的，能够以一定精度模拟出目标物点的真实光谱特性。该系统在动态目标检测识别、艺术品评价复制等领域有着广阔的应用前景。     

基于形式概念分析的多光谱遥感影像波段选择

针对遥感图像特征选择和提取的波段选择,利用形式概念分析中属性约简的思想设计了波段约简选择算法。该方法利用遥感影像的信息量作为指标,将多光谱影像作为形式背景,各波段作为形式属性,通过属性约简得到核心属性即最优波段,这样不仅避免了多光谱遥感数据波段之间的相关性对波段选择的影响,还提高了选取训练样本的效率。最后采用了美国地球观测卫星Landsat拍摄的TM遥感影像数据进行实验,与常用的波段选择方法相比,本文的方法更为简便和高效。     

基于IHS变换的多光谱遥感图像有损压缩算法

提出了一种有效的多光谱遥感图像有损压缩算法,该算法根据多光谱遥感图像的数据特点,将IHS变换和整数小波变换相结合进行数据压缩.采用IHS变换去除谱间相关性;采用整数小波变换去除空间相关性,依据各个子带的重要程度,用自适应阈值进行量化,并分别对量化后的数据和重要图表采用固定比特平面编码和游程编码.实验结果表明,算法在保证平均PSNR≥33dB时,获得了较高的压缩比,并且算法硬件实现简单,对内存的需求低.     

Improvement of color reproduction in color digital holography by using spectral estimation technique

We propose a color digital holography by using spectral estimation technique to improve the color reproduction of objects. In conventional color digital holography, there is insufficient spectral information in holograms, and the color of the reconstructed images depend on only reflectances at three discrete wavelengths used in the recording of holograms. Therefore the color-composite image of the three reconstructed images is not accurate in color reproduction. However, in our proposed method, the spectral estimation technique was applied, which has been reported in multispectral imaging. According to the spectral estimation technique, the continuous spectrum of object can be estimated and the color reproduction is improved. The effectiveness of the proposed method was confirmed by a numerical simulation and an experiment, and, in the results, the average color differences are decreased from 35.81 to 7.88 and from 43.60 to 25.28, respectively.     

Highlight detection and removal from spectral image

We present a constrained spectral unmixing method to remove highlight from a single spectral image. In the constrained spectral unmixing method, the constraints have been imposed so that all the fractions of diffuse and highlight reflection sum up to 1 and are positive. As a result, the spectra of the diffuse image are always positive. The spectral power distribution (SPD) of the light source has been used as the pure highlight spectrum. The pure diffuse spectrum of the measured spectrum has been chosen from the set of diffuse spectra. The pure diffuse spectrum has a minimum angle among the angles calculated between spectra from a set of diffuse spectra and the measured spectrum projected onto the subspace orthogonal to the SPD of the light source. The set of diffuse spectra has been collected by an automated target generation program from the diffuse part in the image. Constrained energy minimization in a finite impulse response linear filter has been used to detect the highlight and diffuse parts in the image. Results by constrained spectral unmixing have been compared with results by the orthogonal subspace projection (OSP) method [Proceedings of International Conference on Pattern Recognition (2006), pp. 812-815] and probabilistic principal component analysis (PPCA) [Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (2005), paper 15]. Constrained spectral unmixing outperforms OSP and PPCA in the visual assessment of the diffuse results. The highlight removal method by constrained spectral unmixing is suitable for spectral images.     

Kernel-based spectral color image segmentation

In this work, we propose a new algorithm for spectral color image segmentation based on the use of a kernel matrix. A cost function for spectral kernel clustering is introduced to measure the correlation between clusters. An efficient multiscale method is presented for accelerating spectral color image segmentation. The multiscale strategy uses the lattice geometry of images to construct an image pyramid whose hierarchy provides a framework for rapidly estimating eigenvectors of normalized kernel matrices. To prevent the boundaries from deteriorating, the image size on the top level of the pyramid is generally required to be around 75 x 75, where the eigenvectors of normalized kernel matrices would be approximately solved by the Nystr?m method. Within this hierarchical structure, the coarse solution is increasingly propagated to finer levels and is refined using subspace iteration. In addition, to make full use of the abundant color information contained in spectral color images, we propose using spectrum extension to incorporate the geometric features of spectra into similarity measures. Experimental results have shown that the proposed method can perform significantly well in spectral color image segmentation as well as speed up the approximation of the eigenvectors of normalized kernel matrices.     

复谱频域OCT系统高分辨率图像重建

为了提高复杂多层样品层析图像的分辨率,构建了复谱频域光学相干层析成像(CSOCT)系统.由于其在低亮度和高速成像方面相对于时域OCT具有更高的灵敏度,因此在光学相干层析成像系统中具有重要的作用.本文对测试样品二层盖波片进行成像实验,基于光学相干层析基本理论,采用五帧相移算法,最终获得测试样品的复谱频域OCT图像.实验结果表明,该系统可以消除谱频域OCT图像中的寄生项和镜像,改善和提高层析图像的分辨率.     

Image spectral amplitude distributions

Models for the probability density functions of the Fourier amplitude of images are derived. The densities are based on a simple model of an image made up of independent objects and incorporates the observed behavior of the circularly averaged power spectrum versus spatial frequency. The density function over all spatial frequencies gives a good fit to spectral amplitude data from a variety of images.     

Maximum a posteriori estimation of spectral reflectance from color image and multipoint spectral measurements

Accurate color image reproduction under arbitrary illumination can be realized if the spectral reflectance functions in a scene are obtained. Although multispectral imaging is one of the promising methods to obtain the reflectance of a scene, it is expected to reduce the number of color channels without significant loss of accuracy. This paper presents what we believe to be a new method for estimating spectral reflectance functions from color image and multipoint spectral measurements based on maximum a posteriori (MAP) estimation. Multipoint spectral measurements are utilized as auxiliary information to improve the accuracy of spectral reflectance estimated from image data. Through simulations, it is confirmed that the proposed method improves the estimation accuracy, particularly when a scene includes subjects that belong to various categories.     

Multimodal medical image fusion based on the spectral total variation and local structural patch measurement

Masses of research decompose the image into different levels of feature maps, but the structures and edges may not appropriately separated. This may cause the loss of image detail in the fusion process. Therefore, we design a robust method for multimodal medical image fusion using spectral total variation transform (STVT). In our method, the source images are first decomposed into a series of texture signatures (referred to as deviation components) and base components via STVT algorithm. Then combine the local structural patch measurement (LSPM) to fuse the deviation components, and the base components are merged using a spatial frequency (SF) dual‐channel spiking cortical model (SF‐DCSCM), in which the SF of base components are regarded as stimulus to activate DCSCM. Finally, the final image is reconstructed by the inverse STVT with the restored images together. Experimental results suggest that proposed scheme achieves promising results, and more competitiveness against some state‐of‐the‐art methods.     

Multivariate image analysis: A review with applications

Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.     

An ontology-based hybrid methodology for image synthesis and identification with convex objects

ABSTRACT

One of the core challenges in developing a computer system for machine learning is to make the system learn efficiently and effectively like a real human by grasping the domain knowledge exemplified by human experts. In this challenge, we have introduced a hybrid image synthesis model that can simulate one of the human’s learning capabilities in the vision field – the ability to synthesize images of convex objects by identifying solid geometries and textures of specific objects using few photographs. We have incorporated an ontology-based, domain knowledge on solid geometries into our model to synthesize large number of training images with only a minimum number of input images. Our initial experiments have shown that our model has convincing improvements by demonstrating a substantially better FAR/FRR/EER results when it is compared with a smaller set of non-synthetic images.