Spatially adaptive multi-resolution multispectral image fusion

Due to the performance limit of remote sensing systems, multispectral images have limited spatial resolution. Their spatial resolution can be improved by merging them with higher resolution image data. A fundamental problem frequently occurring in existing fusion processes, however, is the distortion of spectral information. This paper presents a spatially adaptive image fusion algorithm which produces visually natural images and retains the quality of local spectral information as well. High frequency information of the high resolution image to be inserted to the resampled multispectral images is controlled by adaptive gains to incorporate the difference of local spectral characteristics between the high and the low resolution images into the fusion. Each gain is estimated to minimize the l ₂-norm of the error between the original and the estimated pixel values defined in a spatially adaptive window of which the weights are proportional to the spectral correlation measurements of the corresponding regions. This method is applied to a set of co-registered Landsat 7 Enhanced Thematic Mapper (ETM)+ panchromatic and multispectral image data. The experimental results show that high resolution images can be synthesized by the proposed method, which successfully preserves spectral content of the multispectral images.     

遥感多光谱影像空间分解力增强的融合方法

探讨了采用HIS变换对遥感多光谱影像与航片数字化影像进行融合的方法。试验结果表明所提出的方法是确实可行的。融合后的影像不仅在很大程度上保留了原多光谱影像的光谱特征,而且将其空间分解力提高到原光谱影像的近3倍,因而增强了多光谱影像的判读和量测能力。     

基于2代Curvelet改进IHS变换的遥感图像融合

Curvelet变换是继小波变换之后,能更适合于图像处理的一种新的多尺度变换分析方法,它比小波变换更加适合分析2维图像中的曲线或直线状的边缘特征,同时也具有很强的方向性。为了将该变换应用于图像融合,首先对第2代Curvelet变换理论进行了综述,然后在对基于第2代Curvelet变换的遥感图像融合方法进行研究的基础上,提出了一种与IHS变换结合的融合方法。最后用高分辨率全色图像与低分辨率多谱图像进行了融合实验,实验结果表明,将Curvelet变换引入图像融合,能够更好地提取原始遥感图像的特征,不仅可为融合图像提供更多的信息,而且融合图像能在较好地保留光谱信息的同时,使空间细节信息也得到增强。     

Using low‐spectral‐resolution images to acquire simulated hyperspectral images

We propose a method to acquire simulated hyperspectral images using low‐spectral‐resolution images. Hyperspectral images provide more spectral information than low‐spectral‐resolution images, because of the additional spectral bands used for data acquisition in hyperspectral imaging. Unfortunately, original hyperspectral images are more expensive and more difficult to acquire. However, some research questions require an abundance of spectral information for ground monitoring, which original hyperspectral images can easily provide. Hence, we need to propose a method to acquire simulated hyperspectral images, when original hyperspectral images are especially necessary. Since low‐spectral‐resolution images are readily available and cheaper, we develop a method to acquire simulated hyperspectral images using low‐spectral‐resolution images. With simulated hyperspectral images, we can acquire more ‘hidden’ information from low‐spectral‐resolution images. Our method uses the principles of pixel‐mixing to understand the compositional relationship of spectrum data to an image pixel, and to simulate radiation transmission processes. To this end, we use previously obtained data (i.e. spectrum library) and the sorting data of objects that are derived from a low‐spectral‐resolution image. Using the simulation of radiation transmission processes and these different data, we acquire simulated hyperspectral images. In addition, previous analyses of simulated remotely sensed images do not use quantitative statistical measures, but use qualitative methods, describing simulated images by sight. Here, we quantitatively assess our simulation by comparing the correlation coefficients of simulated images and real images. Finally, we use simulated hyperspectral images, real Hyperion images, and their corresponding ALI images to generate several classification images. The classification results demonstrate that simulated hyperspectral data contain additional information not available in the multispectral data. We find that our method can acquire simulated hyperspectral images quickly.     

小波域上图像非线性扩散滤波

提出一个小波域上图像扩散滤波恢复新模型。主要思想是把原图像作为最精细尺度下的小波子带，根据噪声分布的特点，导出保护较大尺度下信息的泛函模型代替小波阈值除噪，对泛函求变分得：Euler-Lagrange方程。新的滤波方法能避免小波阈值除噪的伪Gibbs现象，改进了同类型非线性扩散方程滤波的效果。利用可加算子分裂(AOS)格式求非线性扩散方程的数值解。实例的数值计算说明对图像滤波和保护边缘的有效性。     

Gray levels can improve the performance of binary image digitizers

The application of gray-scale digitizers to digitization of binary images of straight-edged planar shilhouettes is considered. A measure of digitization-induced ambiguity is introduced. It is shown that if the gray levels are not quantized and the spatial sampling resolution is sufficiently high, error-free reconstruction of the original binary image from the digitized image is possible. When the total bit-count for the representation of the digitized image is limited, i.e., sampling resolution and quantization accuracy are both finite, error-free reconstruction is usually impossible. In this case a bit allocation problem arises, and it is shown that the sensible bit allocation policy is to increase the quantization accuracy as much as possible once a “sufficient” spatial sampling resolution has been reached.     

基于自适应加权的MODIS和SPOT影像融合研究

针对MODIS和SPOT影像融合问题，提出了一种基于自适应加权的亮度相关矩多分辨率影像融合方法。该方法首先对SPOT影像进行小波分解，将MODIS影像由RGB颜色空间变换到IHS颜色空间；然后，根据强度分量和SPOT影像低频分量加权后的局部均值和方差来定义影像亮度相关矩，以选择不同策略进行融合；最后，通过IHS逆变换和小波逆变换来得到包含更多信息和有效特征的融合影像。试验结果证明，该方法得到的融合影像，在保留地物光谱信息和提高空间分辨率上都具有很好的效果。     

利用图像局部自相似性的超分辨率重构算法

图像超分辨率重构是指由低分辨率图像来获得高分辨率图像的过程。为了能够有效地重构出高分辨率图像,提出一种基于图像局部自相似性的超分辨率快速重构算法。该算法首先利用四叉树分割的知识对低分辨率图像进行自适应分块;然后利用低分辨率图像和高分辨率图像在局部区域内的自相似性,由最小二乘方法在各个局部区域自适应的选择插值所需的参数,从而在各个局部区域内进行插值;最后运用小波域的投影算子对插值得到的高分辨率图像进行全局优化,得到最终的高分辨率图像。实验结果表明,由该算法重构的高分辨图像有很好的视觉效果和峰值信噪比。     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

Fusion and registration of THEOS multispectral and panchromatic images

This article presents a new method for the fusion and registration of THEOS (Thailand Earth Observation Satellite) multispectral and panchromatic images in a single step. In the usual procedure, fusion is an independent process separated from the registration process. However, both image registration and fusion can be formulated as estimation problems. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. Here, we concentrate on the relationship between low-resolution multispectral and high-resolution panchromatic imagery. The proposed technique is based on a statistical framework. It employs the maximum a posteriori (MAP) criterion to jointly solve the fusion and registration problem. Here, the MAP criterion selects the most likely fine resolution multispectral and mapping parameter based on observed coarse resolution multispectral and fine resolution panchromatic images. The Metropolis algorithm was employed as the optimization algorithm to jointly determine the optimum fine resolution multispectral image and mapping parameters. In this work, a closed-form solution that can find the fused multispectral image with correcting registration is also derived. In our experiment, a THEOS multispectral image with high spectral resolution and a THEOS panchromatic image with high spatial resolution are combined to produce a multispectral image with high spectral and spatial resolution. The results of our experiment show that the quality of fused images derived directly from misaligned image pairs without registration error correction can be very poor (blurred and containing few sharp edges). However, with the ability to jointly fuse and register an image pair, the quality of the resulting fused images derived from our proposed algorithm is significantly improved, and, in the simulated cases, the fused images are very similar to the original high resolution multispectral images, regardless of the initial registration errors.     

基于自相似性车载采集城市街景图像的重建

大众化的车载为确保实时、高速的图像显示及图像存储,其捕获的图像通常会呈现出较低的分辨率,严重影响了突发状况时有效图像信息的获取。针对该低分辨率的城市街景图像采用了一种基于透视变换、高频补偿的自相似性图像重建方法。该算法在仿射变换的基础上增加了透视变换来进行图像块的匹配,并对每一个匹配的图像块进行高频补偿以恢复构建图像金字塔时丢失的高频信息,通过多尺度非局部方法搜索图像金字塔,合成匹配图像块得到最终的高分辨率图像。采用该算法对采集到的大量低分辨率城市街景图像进行重建,并与ScSR、Upscaling、SCN这三种典型的算法进行对比,实验结果表明该算法在几种盲评价指标上较其他算法好,在提高图像分辨率的同时能保持图像的边缘和细节信息。     

Using pyramids to define local thresholds for blob detection

A method of detecting blobs in images is described. The method involves building a succession of lower resolution images and looking for spots in these images. A spot in a low resolution image corresponds to a distinguished compact region in a known position in the original image. Further, it is possible to calculate thresholds in the low resolution image, using very simple methods, and to apply those thresholds to the region of the original image corresponding to the spot. Examples are shown in which variations of the technique are applied to several images.     

A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation

Image segmentation plays an important role in the computer vision . However, it is extremely challenging due to low resolution, high noise and blurry boundaries. Recently, region-based models have been widely used to segment such images. The existing models often utilized Gaussian filtering to filter images, which caused the loss of edge gradient information. Accordingly, in this paper, a novel local region model based on adaptive bilateral filter is presented for segmenting noisy images. Specifically, we firstly construct a range-based adaptive bilateral filter, in which an image can well be preserved edge structures as well as resisted noise. Secondly, we present a data-driven energy model, which utilizes local information of regions centered at each pixel of image to approximate intensities inside and outside of the circular contour. The estimation approach has improved the accuracy of noisy image segmentation. Thirdly, under the premise of keeping the image original shape, a regularization function is used to accelerate the convergence speed and smoothen the segmentation contour. Experimental results of both synthetic and real images demonstrate that the proposed model is more efficient and robust to noise than the state-of-art region-based models.

     

影像超分辨率重建技术的发展与应用现状

影像超分辨率重建是通过对多幅具有互补信息的低分辨率影像的处理,重构一幅高分辨率影像的技术.从互补信息来源方式、重建模型、快速算法、"硬件"与"软件"相结合、多/高光谱、视频压缩、遥感应用、医学应用和生物信息提取与识别等多个角度阐述了影像超分辨率重建技术的发展与应用现状.     

Spatio-spectral fusion of satellite images based on dictionary-pair learning

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches.     

基于多种变换的遥感图像新型融合方法

针对多光谱图像空间分辨率低这一特点,提出一种在PCA变换基础上,利用小波变换和高通滤波相结合的图像融合算法。实现了ETM+全色波段与ETM+多光谱波段图像的融合,并从空间纹理信息,光谱真实性两个方面进行定性和定量评价。研究表明,该融合算法产生的光谱失真较小,同时很大程度地保持了高分辨率全色波段的空间纹理细节信息,是一种较好的图像融合方法。     

基于Doo Sabin 细分的图像插值

图像插值是放大低分辨率图像以适应目标显示屏幕的一种重要方法。保持图像的几何特征是保证放大图像质量的一个有效途径。基于Doo Sabin细分,提出了一种新的图像插值方法。该方法首先通过一次映射关系获取高分辨图像的部分数据;然后根据高分辨率图像中未知像素点的几何特征将它们分类;再根据Doo Sabin细分方法由已知像素点插值出所有未知像素点。未知像素点的值是与最相关的邻近像素点的加权均值,加权策略根据像素点间的相对位置由Doo Sabin细分推演获得。实验证明,与现有插值方法相比,基于Doo Sabin细分的图像插值能够更好地保持上采样图像的边缘的尖锐特性,减少锯齿现象,获取高质量的高分辨率图像。     

Dedicated image analysis techniques for three-dimensional reconstruction from serial sections in electron microscopy

This article describes the digital treatments needed to reconstruct spatial information at a 40-nm depth resolution of entire cells from transmission electron microscopic images of serial sections containing laser-induced topographical references. The use of a scanning transmission electron microscope (STEM) allows the acquisition of images with high contrast and good resolution at medium magnification. The scanning of our specimens at video frequencies is an attractive way to link a STEM with an image processing system. The magnetic hysteresis of the spools responsible for the electron scanning induces image deformations that have to be modelized and rectified before registering the images corrected for cutting-induced deformations.     

Reversible secret-image sharing with high visual quality

This paper proposes a reversible secret-image sharing scheme for sharing a secret image among 2n shadow images with high visual quality (i.e., they are visually indistinguishable from their original images, respectively). In the proposed scheme, not only can the secret image be completely revealed, but the original cover images can also be losslessly recovered. A difference value between neighboring pixels in a secret image is shared by 2n pixels in 2n shadow images, respectively, where n?≥?1. A pair of shadow images which are constructed from the same cover image are called brother stego-images. To decrease pixel values changed in shadow images, each pair of brother stego-images is assigned a weighted factor when calculating difference values to be shared. A pixel in a cover image is recovered by calculating the average of corresponding pixels in its brother stego-images. A single stego-image reveals nothing and a pair of pixels in brother stego-images reveals partial difference value between neighboring secret pixels. The more brother stego-images are collected, the more information in the secret image will be revealed. Finally, a secret image will be completely revealed if all of its brother stego-images are collected.     

基于多进制小波的多源遥感影像融合

首先介绍了遥感影像融合的一般理论和方法，然后在讨论多进制小波理论和影像特征的基础上，提出了一种基于特征的多进制小波变换的影像融合算法，该算法根据待融合影像分辨率之比来确定采用几进制小波，将待融合的高分辨率影像进行多进制小波变换，然后把高分辨影像经小波变换后获得的低频成分和低分辨率影像依据一定的关系进行相互转换，以形成新的高分辨影像的低频成分，经过多进制小波逆变换获得到融合后的影像，最大限度地利用了待融合影像的信息，防止了影像信息的丢失，通过对具体影像的清晰度和空间分辨率，融合后的影像最大限度地保留了待融合影像的光谱信息，同时提高了待融合影像的清晰度和空间分辨率，给出了SPOT全色影像与SPOT多光谱影像，SPOT全色影像与TM影像的融合结果，并与其他方法进行了比较，从而证明了本方法的优越性和自适应能力。