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.     

基于Shearlet变换的SAR与多光谱遥感影像融合

针对合成孔径雷达（SAR）影像和多光谱遥感影像在融合时空间特征和光谱特征方面不能同时得到较大改善的问题,提出了一种基于成像特性的Shearlet变换域下的多源遥感影像融合方法。利用Shearlet变换的多方向和多尺度分解特性,将多光谱影像和SAR影像分别分解为高频和低频系数,从影像区域能量特征和区域相关性入手,设计了基于区域能量的低频系数融合规则和改进型的脉冲耦合神经网络的高频系数融合规则,使融合结果能够包含更多空间细节信息和光谱信息。利用TerraSAR-X、Landsat5-TM影像进行实验,结果表明该方法在提高影像空间细节表达能力的同时能够较好地融合更多的光谱信息。与小波变换、非下采样轮廓波变换（Nonsubsampled contourlet Transform,NSCT）等方法相比,该方法在空间信息保有量和光谱信息保有量方面都有明显的提升,其中交叉熵有接近100%的提升幅度,互相关系数有高于25%的提升幅度,光谱扭曲度有优于40%的提升幅度。     

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.     

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。     

IKONOS影像的最佳融合技术研究

在研究遥感数字影像融合算法和原理的基础上,选择高空间分辨率的IKONOS遥感影像作为数据源,采用能够以全波段融合的主成分变换、乘积变换、Gram-schmidt变换和小波变换四种融合方法进行比较研究,并通过主观和客观两方面对融合效果进行评价。结果表明,Gram-schmidt变换法融合的IKONOS影像光谱质量最好,同时也较高程度地保持了高几何分辨率的空间信息,是权衡光谱信息和空间信息综合效应最佳的一种方法。     

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.     

Fusion of multispectral and panchromatic Satellite images using the curvelet transform

A useful technique in various applications of remote sensing involves the fusion of different types of satellite images, namely multispectral (MS) satellite images with a high spectral and low spatial resolution and panchromatic (Pan) satellite image with a low spectral and high spatial resolution. Recent studies show that wavelet-based image fusion provides high-quality spectral content in fused images. However, the results of most wavelet-based methods of image fusion have a spatial resolution that is less than that obtained via the Brovey, intensity-hue-saturation, and principal components analysis methods of image fusion. We introduce an improved method of image fusion which is based on the amelioration de la resolution spatiale par injection de structures (ARSIS) concept using the curvelet transform, because the curvelet transform represents edges better than wavelets. Because edges are fundamental in image representation, enhancing the edges is an effective means of enhancing spatial resolution. Curvelet-based image fusion has been used to merge a Landsat Enhanced Thematic Mapper Plus Pan and MS image. The proposed method simultaneously provides richer information in the spatial and spectral domains.     

Multiresolution and Multispectral Data Fusion Using Discrete Wavelet Transform with IRS Images: Cartosat-1, IRS LISS III and LISS IV

Image fusion techniques integrate complimentary information from multiple image sensor data such that the new images are more suitable for the purpose of human visual perception and computer based processing tasks for extraction of detail information. As an important part of image fusion algorithms, pixel-level image fusion can combine spectral information of coarse resolution imagery with finer spatial resolution imagery. Ideally, the method used to merge data sets with high-spatial and highspectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper describes the Discrete Wavelet Transform (DWT) algorithm for the fusion of two images using different spectral transform methods and nearest neighbor resampling techniques. This research paper investigates the performance of fused image with high spatial resolution Cartosat-1(PAN) with LISS IV and Cartosat-1(PAN) sensor images with the LISS III sensor image of Indian Remote Sensing satellites. The visual and statistical analysis of fused images has shown that the DWT method outperforms in terms of Geometric, Radiometric, and Spectral fidelity.     

密集卷积残差网络的遥感图像融合

针对传统的遥感图像融合方法通常会引起光谱失真的问题和大多数基于深度学习的融合方法忽略充分利用每个卷积层信息的不足,本文结合密集连接卷积网络和残差网络的特性,提出了一个新的融合网络。该网络通过建立多个密集卷积块来充分利用卷积层的分级特征,同时块与块之间通过过渡层加快信息流动,从而最大程度地对特征进行极致利用并提取到丰富的特征。该网络应用残差学习拟合深层特征与浅层特征之间的残差,加快网络的收敛速度。实验中利用GaoFen-1（GF-1）和WorldView-2/3（WV-2/3）的多光谱图像MS （Multispectral Image）和全色图像PAN（Panchromatic Image）（MS与PAN的空间分辨率之比为4）评估本文提出方法的有效性。从视觉效果和定量评估结果两个方面来看,本文方法得到的融合结果要优于所对比的传统方法和深度学习方法,并且该网络具有鲁棒性,能够泛化到不需要预训练的其他卫星图像。本文方法通过特征的重复利用实现了光谱信息的高保真并提高了空间细节分辨能力,有利于遥感图像的应用研究。     

Fusion of IKONOS Satellite Imagery Using IHS Transform and Local Variation

The intensity-hue-saturation (IHS) technique is a well-known merging approach for its computational efficiency and spatial definition holding. However, it results in color distortion particularly for the remote sensing images of IKONOS and QuickBird as some other fusion methods, such as principal component analysis, and Brovey transform. Although wavelet-based image fusion approaches can provide a better tradeoff between spatial and spectral quality, the fused images with these methods often have a spatial resolution that is less than that of the IHS-based algorithm. A remote sensing image fusion algorithm based on IHS transform and local variation and its modified approach with low computational complexity are proposed. Visual effect and quantity evaluation results show that the proposed simple algorithm outperforms the conventional image fusion methods in the spectral domain with the spatial quality similar to that of the undecimated wavelet transform-based scheme. The proposed modified method can obtain the similar spatial resolution of the merged image with the IHS-based fusion algorithm and the better spectral quality in the green vegetation areas.      

Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis

Object-based image analysis (OBIA) has attained great importance for the delineation of landscape features, particularly with the accessibility to satellite images with high spatial resolution acquired by recent sensors. Statistical parametric classifiers have become ineffective mainly due to their assumption of normal distribution, vast increase in the dimensions of the data and availability of limited ground sample data. Despite pixel-based approaches, OBIA takes semantic information of extracted image objects into consideration, and thus provides more comprehensive image analysis. In this study, Indian Pines hyperspectral data set, which was recorded by the AVIRIS hyperspectral sensor, was used to analyse the effects of high dimensional data with limited ground reference data. To avoid the dimensionality curse, principal component analysis (PCA) and feature selection based on Jeffries–Matusita (JM) distance were utilized. First 19 principal components representing 98.5% of the image were selected using the PCA technique whilst 30 spectral bands of the image were determined using JM distance. Nearest neighbour (NN) and random forest (RF) classifiers were employed to test the performances of pixel- and object-based classification using conventional accuracy metrics. It was found that object-based approach outperformed the traditional pixel-based approach for all cases (up to 18% improvement). Also, the RF classifier produced significantly more accurate results (up to 10%) than the NN classifier.     

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 GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.     

Multi-Feature Classification Approach for High Spatial Resolution Hyperspectral Images

High spatial resolution hyperspectral images not only contain abundant radiant and spectral information, but also display rich spatial information. In this paper, we propose a multi-feature high spatial resolution hyperspectral image classification approach based on the combination of spectral information and spatial information. Three features are derived from the original high spatial resolution hyperspectral image: the spectral features that are acquired from the auto subspace partition technique and the band index technique; the texture features that are obtained from GLCM analysis of the first principal component after principal component analysis is performed on the original image; and the spatial autocorrelation features that contain spatial band X and spatial band Y, with the grey level of spatial band X changing along columns and the grey level of spatial band Y changing along rows. The three features are subsequently combined together in Support Vector Machine to classify the high spatial resolution hyperspectral image. The experiments with a high spatial resolution hyperspectral image prove that the proposed multi-feature classification approach significantly increases classification accuracies.     

基于亮度相关矩的MODIS和SPOT影像融合研究

针对MODIS影像空间分辨率较低的问题，提出了一种基于亮度相关矩的多分辨率图像融合方法。该方法首先对SPOT影像进行小波分解，将MODIS影像构成的RGB颜色系统变换到IHS颜色系统；然后，根据强度分量和SPOT影像低频分量的均值和方差来定义图像亮度相关矩；最后，IHS逆变换和小波逆变换得到包含更多信息和有效特征的融合图像。试验结果证明该方法得到的融合图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。     

影像局部直方图匹配滤波技术用于遥感影像数据融合

本文提出了一种基于局部直方图匹配波技术的影像融合新方法。该方法分析了局部影像统计特性,应用均值或均值－方差匹配正态函数,对要融合的两幅影像局部直方图进行匹配。文中给出了３种不同的融合算法（高通滤波法,局部均值匹配法和局部均值与方差匹配法）用于ＳＰＯＴ全色影像和ＴＭ多光谱影像融合的结果,并进行了比较,质量评价结果证明了提出的方法在提高了原ＴＭ多光谱影像空间分辨率的同时,较好地保留了多光谱影像频道的光     

A comparative data-fusion analysis of multi-sensor satellite images

Remote-sensing data play an important role in extracting information with the help of various sensors having different spectral, spatial and temporal resolutions. Therefore, data fusion, which merges images of different spatial and spectral resolutions, plays an important role in information extraction. This research investigates quality-assessment methods of multisensor (synthetic aperture radar [SAR] and optical) data fusion. In the analysis, three SAR data-sets from different sensors (RADARSAT-1, ALOS-PALSAR and ENVISAT-ASAR) and optical data from SPOT-2 were used. Although the PALSAR and the RADARSAT-1 images have the same resolutions and polarisations, images are gathered in different frequencies (L and C bands, respectively). The ASAR sensor also has C-band radar, but with lower (25 m) resolution. Since the frequency is a key factor for penetration depth, it is thought that the use of different SAR data might give interesting results as an output. This study describes a comparative study of multisensor fusion methods, namely the intensity-hue-saturation, Ehlers, and Brovey techniques, by using different statistical analysis techniques, namely the bias of mean, correlation coefficient, standard deviation difference and universal image quality index methods. The results reveal that Ehlers' method is superior to the others in terms of spectral and statistical fidelity.     

单样本对卷积神经网络遥感图像时空融合

遥感图像时空融合是一种生成兼具高时空分辨率的合成遥感数据的技术。近年来,产生了一些基于卷积神经网络的时空融合方法。这些方法效果良好,但需要较多的图像样本对训练模型,限制了它们的应用。针对此问题,本文提出了一种单样本对卷积神经网络时空融合方法（SS-CNN）。该方法以高空间分辨率图像的波段平均图像提供的空间信息激励卷积神经网络建立高、低空间分辨率图像间的超分关系,进而利用该超分关系映射求解目标高空间分辨率图像。在实验中使用两个模拟数据集和一个真实数据集对该方法进行了测试,并与两种常用的时空融合方法做了比较。实验结果表明,SS-CNN在单样本对训练的情况下,可以较好地预测地物的物候变化和类型的变化,且在异质性高、地块破碎的区域表现良好。其不足之处在于会在地物边界上会造成轻微的模糊,将来需针对此问题做进一步改进。     

Exploiting IKONOS and Hyperion data fusion for automated road extraction

We present here the examples that show how fusing data from hyperspectral sensors with data from high spatial resolution sensors can enhance overall road detection accuracy. The fusion of hyperspectral and high spatial resolution data combines their superior respective spectral and spatial information. IKONOS (MSS) and Hyperion images were fused using the principal component analysis (PCA) method. The approach for road extraction integrates multiresolution segmentation and object oriented classification. Road extraction is done from an IKONOS (MSS) image and a Hyperion and IKONOS (MSS) merged image and comparisons are made depending on accuracy and quality measures such as completeness and correctness. This article also emphasises the types of roads which are giving better accuracy of extraction after fusion with hyperspectral image. This can vary because of types of material and condition of roads. The methodology was applied on roads of Dehradun, India.     

提高中巴卫星IR MSS图像空间分辨能力的光谱保真融合方法

介绍一种提高中巴资源卫星IRMSS图像空间分辨能力的光谱保真融合方法。通过计算低分辨率图像上每一个像元对应的高分辨率图像上一组子像元的平均亮度值及二者之差,将该差值与高分辨率图像上相应子像元亮度求和,形成新的图像。该图像具有高分辨率图像的空间细节,又具有低分辨率图像的光谱信息,从而实现融合图像信息保真。试验表明,光谱保真融合方法可以在不改变光谱信息的前提下提高IRMSS图像的空间分辨能力,是一种新的简单实用的数据处理方法。