结合NSCT和KPCA的高光谱遥感图像去噪

针对高光谱遥感图像易受噪声干扰,本文提出了一种基于非下采样Contourlet变换NSCT(Nonsubsampled Contourlet Transform)和核主成分分析KPCA(Kernel Principal Component Analysis)的去噪方法。首先对高光谱各波段图像进行NSCT分解;然后利用KPCA对NSCT系数进行处理,并在KPCA重构时依据各类噪声的特性选取合适的主成分;最后用处理过的系数进行逆变换得到去噪图像。实验结果表明,本文方法抑制了高光谱遥感图像中的噪声干扰,较完整地保留了原始数据的有效信息。     

一种顾及区域特征差异的热红外与可见光图像多尺度融合方法

针对传统的热红外与可见光图像融合方法对比度低,容易出现边缘细节、目标等信息丢失或减弱的现象,提出一种顾及区域特征差异的热红外与可见光图像多尺度融合方法。首先采用自适应PCNN（脉冲耦合神经网络）模型和二维Renyi熵相结合的图像分割方法,分别对红外和可见光图像进行区域分割;然后利用非下采样Contourlet变换对原图像进行多尺度多方向分解,根据区域的特征差异设计不同的融合规则,融合热红外与可见光图像。实验结果表明,该方法不仅能有效地融合热红外图像的目标特征,还能更多地保留可见光图像丰富的背景信息,融合图像对比度高,在视觉效果和客观评价上优于传统融合方法。     

联合NSCT与多重分形的高噪声侧扫声呐图像分割

针对现有分割算法对高噪声侧扫声呐图像分割准确率低的问题,提出了一种综合利用NSCT(non-subsampled contourlet transform)分解图像、局部标准差和均值组合增强图像和多重分形判断图像奇异性的侧扫声呐图像分割方法。首先,借助NSCT分解图像,获得滤除高频噪声且保留轮廓信息的低频图像和一系列高频方向子带图像。然后,基于侧扫声呐图像中目标及其阴影伴随出现的特点,计算低频图像的局部标准差与均值的组合特征,获得分别突显目标及其阴影的特征图,使用多重分形分割方法分割特征图,获得低频图像分割结果;利用图像差分和非极大值抑制方法分割高频方向子带图像,获得高频分割结果;融合高低频分割结果获得目标及其阴影的精细边缘。最后通过试验验证了本文方法的有效性。     

Satellite Image Resolution Enhancement Using Nonsubsampled Contourlet Transform and Clustering on Subbands

In this paper two new schemes for resolution enhancement (RE) of satellite images are proposed based on Nonsubsampled Contourlet Transform (NSCT). First one is based on the interpolation on band pass images obtained by applying NSCT on the input low resolution image. Similar to Demirel and Anbarjafari (IEEE Trans Geosci Remote Sens 49(6):1997–2004, 2011), as an intermediate step, the difference between approximation band and the input low resolution image is added with all the band pass directional subbands, to obtain a sharper image. This method is simple and computationally efficient but lacks sharp recovery of the edges due to the interpolation of band pass images. To overcome this, another method is proposed to obtain the difference layer, where dictionary is built using patches which are extracted from high resolution training image subbands. Similar patches from the dictionary are then clustered together. This method gives a much sharper image than the first method. Subjective and objective analysis of proposed methods reveals the superiority of the methods over conventional and other state-of-the-art RE methods.     

A new pan-sharpening method using multiobjective particle swarm optimization and the shiftable contourlet transform

In this paper, a novel approach based on multiobjective particle swarm optimization (MOPSO) is presented for panchromatic (Pan) sharpening of a multispectral (MS) image. This new method could transfer spatial details of the pan image into a high-resolution version of the MS image, while color information from the low-resolution MS image is well preserved. The pan and MS images are locally different because of different resolutions, and therefore we cannot directly combine them in the spatial domain. For this reason, we generate two initial results, which are more appropriate for a weighted combination. First, the pan and the MS images are histogram matched. Then we use the shiftable contourlet transform (SCT) to decompose the histogram-matched pan and MS images. The SCT is a new shiftable and modified version of the contourlet transform. In this step, an algorithm based on the SCT is used to generate two initial results of the high-resolution MS images. Our objective is to produce two modified high-resolution MS images, in which one has high spatial similarity to the pan image and the other one has high radiometric quality in each band. Therefore, we have used two different fusion rules to integrate the high-frequency contourlet coefficients of the pan and MS images to generate two initial results of high-resolution MS image or the pan-sharpened (PS) image. Finally, we can find the optimal PS image by applying the MOPSO algorithm and using the two initial PS results. Specifically, the PS image is obtained via a weighted combination of the two initial results, in which the weights are locally estimated via a multiobjective particle swarm optimization algorithm to generate a PS image with high spatial and radiometric qualities. Based on experimental results obtained, the produced pan-sharpened image also has good spectral quality. The efficiency of the proposed method is tested by performing pan-sharpening of high-resolution (Quickbird and Wordview2) and medium-resolution (Landsat-7 ETM +) datasets. Extensive comparisons with the state-of-the-art pan-sharpening algorithms indicate that our new method provides improved subjective and objective results.     

基于NSCT和SURF的遥感图像匹配

SURF(Speed Up Robust Features)算法是对尺度不变特征变换SIFT(Scale Invariant Feature Transform)算法的一种改进,应用到遥感图像匹配领域中可以大大提高匹配速度,但是匹配精度略有下降。为此,本文提出一种基于无下采样Contourlet变换NSCT(Nonsubsampled Contourlet Transform)和SURF的遥感图像匹配算法。首先使用NSCT分别分解参考图像和待匹配图像,得到各自对应的低频分量;然后把这两幅低频分量图像作为SURF算法的输入图像进行预匹配,降低高频噪声对匹配结果的影响;最后利用预匹配结果求解变换模型的参数,并采用随机抽样一致RANSAC(Random Sample Consensus)算法剔除误匹配点对,解决了SURF算法存在的错误匹配问题。实验结果表明,与SIFT算法、SURF算法相比,本文算法具有更高的匹配精度和更快的匹配速度,且抗旋转、噪声、亮度变化能力更强。     

基于非下采样Contourlet变换的极化SAR图像融合与去噪

针对非下采样Contourlet变换(NSCT)在处理噪声影像中具有的优势,以及同极化SAR图像(HH、VV)之间的相关性与互补性,本文实验了一种基于非下采样Contourlet变换的极化图像融合方法。该方法首先对每个极化图像进行多尺度、多方向分解,然后对不同分解子带系数分别采用有利于斑点噪声去除和信息增强的融合规则进行融合,最终通过NSCT反变换得到融合图像。通过信息熵、相关系数以及等效视数等指标的评价,验证了该方法可以有效地实现信息增强,同时该方法也在一定程度上降低了斑点噪声的负面影响。     

基于最大后验和非局域约束的非下采样轮廓波变换域SAR图像去噪方法

本文提出了一种基于最大后验和非局域约束的非下采样轮廓波变换域SAR图像去噪方法。根据SAR图像数据的特征,引入了非对数加性模型,并在该模型下对SAR图像NSCT域中的噪声分布统计建模,应用最大后验（MAP）准则和Non-Local（NL）约束相结合的方法解求SAR图像真实信号的NSCT系数。实验结果表明,本方法具有良好的去噪能力并在性能上优于当前主流方法。     

Improved Image Pansharpening Technique using Nonsubsampled Contourlet Transform with Sparse Representation

Multispectral (MS) and panchromatic (PAN) images contains complementary information. High spatial and spectral resolution is a prerequisite for images to be useful, which can be achieved through image pansharpening. In this paper, we propose a new pansharpening technique which is a combination of nonsubsampled contourlet transform (NSCT) and sparse representation (SR), called NSCT–SR. NSCT is a shift-invariant version of the contourlet transform which combines nonsubsampled pyramid (NSP) and the directional filter banks. NSP splits input MS and PAN images into low-pass and high-pass sub-bands. Fusion of high-pass sub-bands is done using local energy information while low-pass sub-bands are fused using SR. Finally, fused low-pass and high-pass sub-bands are combined to obtain image with high spatial and high spectral resolution. We have quantitatively compared NSCT–SR with other multiresolution algorithms by calculating spatial and spectral quality parameters. It is observed that spatial quality is improved by 0.93 % (for seaside image) and 1.54 % (for urban image). While spectral quality is improved maximum up to 31.39 and 40.47 %, for respective images. NSCT–SR also compared with other state-of-art algorithms by calculating various performance parameters including quality with no reference. It is found that, overall; NSCT–SR performs better compared to algorithms considered in work.     

混沌蜂群优化的NSST域多光谱与全色图像融合

为有效融合多光谱图像的光谱信息和全色图像的空间细节信息,提出了一种基于混沌蜂群优化和改进脉冲耦合神经网络(PCNN)的非下采样Shearlet变换(NSST)域图像融合方法。首先对多光谱图像进行Intensity-HueSaturation(IHS)变换,全色图像的直方图按照多光谱图像亮度分量的直方图进行匹配;然后分别对多光谱图像的亮度分量和新全色图像进行NSST变换,对低频分量使用改进加权融合算法进行融合,以互信息作为适应度函数,利用混沌蜂群算法找到最优加权系数。对高频分量采用改进脉冲耦合神经网络(PCNN)方法进行融合,再经NSST逆变换和IHS逆变换得到融合图像。本文方法在主观视觉效果和信息熵、光谱扭曲度等客观定量评价指标上优于基于IHS变换、基于非下采样Contourlet变换(NSCT)和非负矩阵分解(NMF)、基于NSCT和PCNN等5种融合方法。本文方法在提升图像空间分辨率的同时,有效地保留了光谱信息。     

结合多尺度几何分析和KICA的遥感图像变化检测

为了进一步提高基于独立分量分析ICA(Independent Component Analysis)的遥感图像变化检测精确度,更好地实现地表覆盖的动态监测,将多尺度几何分析和核独立分量分析KICA(Kernel Independent Component Analysis)相结合应用于遥感图像的地表覆盖变化检测。首先利用Contourlet变换、复Contourlet变换CCT(Complex Contourlet Transform)、非下采样Contourlet变换NSCT(Nonsubsampled Contourlet Transform)等多尺度几何分析对土地遥感图像进行多尺度分解;然后对分解后的数据进行核独立分量分析,通过核函数将数据映射到高维特征空间中,再在该空间中用ICA方法分离出互相独立的分量;最后将分离后的地表变化分量转化为图像分量,再采用最大类间方差法对变化图像进行分割,实现地表覆盖的变化检测。给出了本文方法和近年来提出的基于ICA、基于KICA、基于小波变换和ICA等变化检测方法的实验结果,并进行了分析和定量比较。实验结果表明,基于多尺度几何分析和KICA的变化检测方法能更好地分离出遥感图像的变化信息,其中基于NSCT和KICA方法的错判和漏检误差最小,且在边缘细节方面处理得更好,而基于Contourlet变换和KICA方法的检测效率相对较高。     

基于改进粗糙集和NSCT的红外遥感图像增强

提出了一种基于改进粗糙集和NSCT的红外遥感图像增强方法。该方法首先利用NSCT对图像进行分解,得到多层多方向子带系数;然后根据相邻尺度和不同方向的子带中图像噪声、脆弱边缘等不同成分的系数分布,使用粗糙集对其进行分块,并制定合理的决策规则;再通过集合运算对系数中不同子块采用不同的处理方法,一方面抑制噪声,另一方面保护图像中的脆弱边缘结构,并采用增强函数对其进行不同程度的增强;最后对处理过的NSCT系数进行重构,获得增强后的红外图像。实验表明,该算法相对于其他传统红外遥感图像增强算法具有较好效果。     

以SRTM-DEM为控制的光学卫星遥感立体影像正射纠正

针对全球测图缺少统一的控制基准的问题,提出了利用SRTM-DEM作为控制基准,对光学卫星遥感影像进行正射纠正的方法。首先,对光学卫星影像构建的立体影像对进行自由网平差并制作DEM;然后,以SRTM-DEM作为控制,将DEM作为单元模型,进行独立模型法DEM区域网平差,获得单元模型的定向参数;最后,改正立体影像的成像几何模型参数,进行正射纠正。选取湖北咸宁和江西某地两个测区的资源三号数据进行试验,试验结果表明,资源三号正视全色影像的平面定向精度由12.93像素提高到6.85像素。     

利用NSCT和Krawtchouk矩进行图像检索

提出了一种基于非下采样Contourlet变换(nonsubsampled contourlet transform,NSCT)和Krawtchouk矩的图像检索算法。首先,通过NSCT对图像进行分解,提取每个分解层次上不同方向子带系数分布的数学特征作为图像的纹理特征;然后,利用Krawtchouk矩描述图像的形状特征;最后,根据加权的相似性度量实现图像检索。实验结果表明,所提取的特征向量具有平移、旋转、尺度不变性,且能获得更高的检索精度。     

基于布谷鸟算法的遥感影像智能分类

提出了一种新的基于布谷鸟算法的智能式遥感分类方法。采用布谷鸟智能优化算法,自动搜索遥感影像各波段的最优阈值分割点,并定义各波段最优阈值分割点和影像分类目标类别的连线为布谷鸟的最佳解,构造以If-Then形式表达的遥感分类规则。将所提的基于布谷鸟算法的影像分类方法应用于ALOS影像分类中,并与蜂群智能遥感分类方法和See5.0决策树方法进行了对比分析。结果表明,布谷鸟智能遥感分类的总体精度和Kappa系数均比蜂群智能遥感分类和See5.0决策树方法更高,该智能遥感分类方法具有更好的分类效果。     

大面阵数字航空相机DMZ影像预处理研究

对国产新型内拼式大面阵数字航测相机DMZ原型系统获取的数字影像进行几何预处理的方法进行了研究;在分析DMZ全色成像光路特点的基础上,提出了多相机联合的影像几何校正与缝合方法,生成等效虚拟立体影像。利用等效虚拟立体影像进行了3种布控方案的数字空三平差和精度分析,验证了该方法的正确性和可靠性,实现了对DMZ影像预处理的探索性研究。     

非下采样Contourlet域的ICA法SAR图像相干斑抑制

相干斑是SAR图像固有信息,也是SAR图像处理研究的重要方面之一.将非下采样Contourlet变换和统计信号处理中的独立分量分析相结合进行斑点抑制.对SAR图像进行非下采样金字塔和非下采样方向性滤波器组分解,在分解得到的非下采样Contourlet变换域利用扩展Infomax算法分离SAR图像斑点噪声.实验结果表明,...     

Remote Sensing Image Fusion Based on Nonlinear IHS and Fast Nonsubsampled Contourlet Transform

The purpose of remote sensing image fusion is to inject the detail image extracted from the panchromatic (PAN) image into the low spatial resolution multispectral (MS) image. A novel remote sensing image fusion method based on fast nonsubsampled contourlet transform (FNSCT) and Nonlinear intensity-hue-saturation (IHS) is presented in this paper. Firstly, the Nonlinear IHS transform is performed on the multispectral image, and then the I-component representing the spatial resolution and the panchromatic image is transformed by NSCT to obtain the low frequency and high frequency. Finally, the coefficients are selected using the improved sum-modified-Laplacian (SML) method and the improved Log-Gabor filter in the low frequency and the high frequency, respectively. Experimental results show that the proposed method is the most advanced fusion method in subjective and objective evaluation, can provide more spatial information, and retain more spectral information compared with several other methods.     

Spectral and Spatial Quality Analysis in Pan Sharpening Process

Image fusion is a process to obtain new images containing more information by combining images obtained same or different sensors. With most of the earth observation satellites, high spatial resolution panchromatic images and low spatial resolution multispectral images are obtained. As an example of image fusion ??pan sharpening?? is a process of combining of high spatial resolution panchromatic images and low spatial resolution multispectral images. At the end of the fusion process both high spatial and spectral resolution new images are obtained. In this study, panchromatic and multispectral images gathered from Ikonos were used. Panchromatic and multispectral images belonging to the same sensor were combined by using different image fusion methods. As pan sharpening methods Brovey transform, Modified IHS, Principal Component Analysis (PCA), Wavelet PC transform and Wavelet A Trous transformation methods were used. Quality of fused products was evaluated from the point of view of both visual and statistical criteria. While wavelet based methods are succesfull in terms of protection of spectral quality of original multispectral images, the colorbased and statistical methods are giving better results within the improvement of spatial content.     

Lithological Mapping in the Eastern Part of the Central Iranian Volcanic Belt Using Combined ASTER and IRS data

The study area is located in the eastern part of the central Iranian volcanic belt. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS ) pan images were used for applying several image classification methods for lithological mapping. ASTER visible-near infrared and shortwave infrared bands were sharpened using IRS pan image. We used classification methods such as Maximum likelihood, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) in order to evaluate the usefulness of these methods for geological mapping. The classification results showed that MLC has the best accuracy and the classified image closely resembles the previously prepared geology map of the area.