基于小波理论的IKONOS卫星全色影像和多光谱影像的融合

1999年9月24日发射成功的世界上第一颗商用1m分辨率的卫星IKONOS，具有1m分辨率的全色影像和4m分辨率的多光谱影像，通过对IKONOS1m分辨率的全色影像和4m分辨率的多光谱影像的融合可以获得1m分辨率的多光谱影像，为应用提供理高质量的数据源，基于小波多分辨率分析的MRAGM方法，它适合处理任意整数分辨率之比的融合情况，具有在提高影像空间分辨率的同时又保持色调和饱和度不变的优越性，而实现它的关键是构建具有紧支撑特性的M进制低通尺度函滤波器，本文利用基于四进制小波滤波器的MRAGM算法，成功地把全色影像和多光谱影像进行了融合，结果显示该方法的效果令人满意。     

考虑人类视觉特征的融合图像评价方法

图像质量评价是遥感图像融合必不可少的环节,对融合图像的客观评价具有较好的研究价值,好的客观评价指标应尽量与主观评价一致。针对全色图像和多光谱图像的融合质量评价,在考虑人类视觉系统的多通道分解和对比度敏感特性的基础上,首先修改颜色失真和结构相似度来分别评价光谱信息和空间信息,然后综合两者作为融合图像的整体评价。以SPOT5图像和高分二号卫星图像为原始数据,对7种融合图像的质量进行了评价。实验表明,对于高分辨率的遥感图像,这种引入了人类视觉特征的融合评价方法与主观评价有很好的一致性。     

结合Gram-Schmidt变换的高光谱影像谐波分析融合算法

针对高光谱影像谐波分析融合(HAF)算法在影像融合时不顾及地物光谱曲线整体反射率这一缺陷,提出了结合Gram-Schmidt变换的高光谱影像谐波分析融合(GSHAF)改进算法。GSHAF算法可在完全保留融合前后像元光谱曲线波形形态的基础上,将高光谱影像融合简化为各像元光谱曲线的谐波余相组成的二维影像与高空间分辨率影像之间的融合。它是在原始高光谱影像光谱曲线被谐波分解为谐波余项、振幅和相位后,首先将其谐波余项与高空间分辨率影像进行GS变换融合,这样便可有效地修正融合后像元光谱曲线的反射率特征,随后再利用该融合影像与谐波振幅、相位进行谐波逆变换,完成高光谱影像谐波融合。本文最后利用Hyperion高光谱遥感影像与ALI高空间分辨率影像对GSHAF算法进行可行性分析,再以HJ-1A等卫星数据对其进行普适性验证,试验结果表明,GSHAF算法不仅可以完全地保留光谱曲线波形形态,而且融合后影像的地物光谱曲线反射率更接近真实地物。     

Analyzing hyper-spectral and multi-spectral data fusion in spectral domain

Image fusion is the combination of two or more different images to form a new image by using a certain algorithm. Despite the fact that the number and kind of satellite imagery are daily increasing, using fusion techniques, in a proper way, to eliminate the redundancy in data and increase the quality of data is an important challenge in Remote Sensing Image Processing. Fusion of multispectral images with a hyperspectral image generates a composite image which preserves the spatial quality from the high resolution (MS) data and the spectral characteristics from the hyperspectral data. For the present study three fusion algorithms (Principal Component Transformation, Colour Normalized and Gram-Scmidt Transformation) were analysed for Hyperion and IKONOS MSS data. Their ability to preserve the spectral quality of fused data, in comparison with original hyper-spectral image, has been investigated.     

基于GF2号卫星影像的农业信息提取方法对比分析

以GF2卫星0.8 m全色/3.2 m多光谱分辨率遥感影像为基础数据源,对基于GF2号卫星影像的农业信息提取流程和方法进行了研究与对比分析。首先对GF2号卫星影像进行波谱分析;其次对GF2号影像进行融合,并对多种融合方法进行质量评价;最后选择阈值法、波谱间关系法、非监督分类法和面向对象法分别对GF2号影像数据进行农业信息提取试验,并对信息提取结果进行精度验证和结果分析。试验表明,面向农业信息提取的GF2号卫星影像融合方法中,Pansharp融合算法融合影像色彩正常,无虚影,清晰度高,地类对比度正常,纹理清晰,熵值及与原始多光谱影像的相关系数高。阈值法和谱间关系法适用于提取单要素农业信息,非监督分类法能够初步获取研究区土地利用情况,面向对象法提取研究区全要素信息精度高。总体来说,不同信息提取方法具有各自的优势,在具体实际应用中,可以根据目标地类的波谱特性,选择适宜的遥感影像处理和信息提取方法。     

基于经验模态分解的高分辨率影像融合

文章提出基于经验模态分解（Emp iricalMode Decomposition,EMD）的特征层影像融合模型。对多光谱波段影像进行IHS变换获得强度影像,采用行列分解实现一维经验模态分解的二维拓展,并用于分离高分辨波段影像与强度影像的细节特征信息,对高分辨率波段影像的高频与强度影像波段的低频进行重构获得融合后的强度影像,再通过IHS反变换获得融合影像。文章介绍了经验模态分解的基本原理,定义了经验模态分解的多尺度分解与合成结构,提出融合模型的技术路线。选择UICKB IRD影像的全色波段与多光谱波段进行融合实验,根据典型行（列）的EMD分析,确定经验模量的取舍尺度,按提出的融合路线获得融合影像,并与小波融合,IHS融合,Brovey融合模型获得的影像进行视觉及量化比较。选择信息熵、标准差指标对融合影像的空间细节信息进行评价,同时选择平均灰度值、相关系数、偏差指数评价融合影像的光谱扭曲程度,结果表明本融合模型最优。     

多源信息融合中小波变换的应用研究

研究了小波变换在多源信息融合中的应用,主要涉及高分辨率全色影像与低分辨率多光谱影像融合问题及合成孔径雷达与光学影像的融合问题.主要方法是基于地物光谱信息特征的彩色融合与基于几何特征的融合.利用小波技术对整个融合过程加以改进.获得的融合结果表明基于光谱特征信息的融合方法,可以有效地提高多光谱影像的空间分辨率,而基于几何特征的融合方法,可以提高对遥感影像的目视解译效果.视觉效果上就是将高分辨率影像的细节加入到了低分辨率多光谱影像中,并同时保持原始影像的光谱特征.     

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.     

Assessing Optimal Image Fusion Methods for Very High Spatial Resolution Satellite Images to Support Coastal Monitoring

This study examines best image fusion approaches for generating pansharpened very high resolution (VHR) multispectral images to be utilized for monitoring coastal barrier island development. Selected fusion techniques assessed in this research come from the three categories of spectral substitution (e.g., Brovey transform and multiplicative merging), arithmetic merging (e.g., modified intensity-hue-saturation and principal component analysis), and spatial domain (e.g., high-pass filter, and subtractive resolution merge). The image fusion methods selected for this study were capable of producing pansharpened VHR images with more than three bands. Comparisons of fusion techniques were applied to images from three satellite sensors: United States commercial satellites IKONOS and QuickBird, and the Korean KOMPSAT II. Pansharpened VHR multispectral images were assessed by spectral and spatial quality measurements. Results satisfying both spectral and spatial quality revealed optimum pansharpened techniques necessary for regular coastal mapping of barrier islands. These techniques may also be used to assess the quality of recently available VHR imagery acquired by numerous international, government, and commercial VHR satellite programs.     

利用融合方法提升WorldView短波红外影像分辨率

WorldView卫星在8个可见光-近红外多光谱波段的基础上,新增加的8个短波红外（简称SWIR）影像,大大提高了地物信息提取能力。但短波红外影像分辨率与多光谱影像相比分辨率过低,影响应用效果。本文提出了一种结合主分量变换和非下采样小波变换的影像融合方法来提升WorldView短波红外影像的空间分辨率。定量指标和目视评价证明本文提出的融合方法具有较好的融合效果,能够在显著提升短波红外影像空间分辨率的同时很好地保持原始光谱特性。     

利用融合方法提升WorldVeiw短波红外影像分辨率

WorldView卫星在8个可见光-近红外多光谱波段的基础上,新增加的8个短波红外(简称SWIR)影像,大大提高了地物信息提取能力.但短波红外影像分辨率与多光谱影像相比分辨率过低,影响应用效果.本文提出了一种结合主分量变换和非下采样小波变换的影像融合方法来提升WorldView短波红外影像的空间分辨率.定量指标和目视评价证明本文提出的融合方法具有较好的融合效果,能够在显著提升短波红外影像空间分辨率的同时很好地保持原始光谱特性.     

A Tool Assessing Optimal Multi-Scale Image Segmentation

Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this study, we developed an optimum scale parameter selector (OSPS) tool for objective determination of multiple optimal scales in an image by MRS using eCognition software. The ready to use OSPS tool consists of three modules and determines optimum scales in an image by combining intrasegment variance and intersegment spatial autocorrelation. The tool was tested using WorldView-2 and Resourcesat-2 LISS-IV Mx images having different spectral and spatial resolutions in two areas to find optimal objects for ground features such as water bodies, trees, buildings, road, agricultural fields and landslides. Quality of the objects created for these features using scale parameters obtained from the OSPS tool was evaluated quantitatively using segmentation goodness metrics. Results show that OSPS tool is able determine optimum scale parameters for creation of representative objects from high resolution satellite images by MRS method.     

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     

SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images

Image fusion techniques are widely used for remote sensing data. A special application is for using low resolution multi-spectral image with high resolution panchromatic image to obtain an image having both spectral and spatial information. Alignment of images to be fused is a step prior to image fusion. This is achieved by registering the images. This paper proposes the methods involving Fast Approximate Nearest Neighbor (FANN) for automatic registration of satellite image (reference image) prior to fusion of low spatial resolution multi-spectral QuickBird satellite image (sensed image) with high spatial resolution panchromatic QuickBird satellite image. In the registration steps, Scale Invariant Feature Transform (SIFT) is used to extract key points from both images. The keypoints are then matched using the automatic tuning algorithm, namely, FANN. This algorithm automatically selects the most appropriate indexing algorithm for the dataset. The indexed features are then matched using approximate nearest neighbor. Further, Random Sample Consensus (RanSAC) is used for further filtering to obtain only the inliers and co-register the images. The images are then fused using Intensity Hue Saturation (IHS) transform based technique to obtain a high spatial resolution multi-spectral image. The results show that the quality of fused images obtained using this algorithm is computationally efficient.     

利用多光谱影像检测资源三号卫星平台震颤

卫星平台震颤是影响高分辨率卫星成像质量的因素之一,会引起影像模糊和内部畸变。本文从资源三号卫星多光谱相机的成像特点和多光谱影像配准误差影响因素入手,理论推导和仿真分析了卫星平台震颤对配准误差的影响规律,在此基础上提出了基于多光谱影像高精度密集匹配的平台震颤检测方法和流程,最后利用不同波段、不同时间的成像数据进行试验。试验结果表明资源三号卫星在试验数据成像阶段存在约0.6Hz的平台震颤,且垂轨方向震颤幅值大于沿轨方向,同时引起波段间相同频率周期性配准误差。检测结果为进一步提高资源三号处理精度提供了可能,也为卫星平台震颤源的分析和优化卫星平台设计提供了重要参考依据。     

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.     

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.     

Improved IHS Algorithm for Fusing High Resolution Satellite Images of Urban Areas

The intensity-hue-saturation method is used frequently in image fusion due to its efficiency and high spatial quality. The main shortage is its spectral distortion stemmed from replacement of intensity band with higher resolution image. In this study, a new method is introduced to improve the spectral quality of the Intensity-Hue-Saturation (IHS) algorithm. The goal of this study is to produce the fused image that has a better spectral and spatial quality with respect to the original images in term of visual comparison and the classification result. In this regard, an improved statistical approach is developed to combine an intensity band from IHS algorithm and an input high resolution image such as SAR or Panchromatic image. Then the intensity image is replaced by the combined image band. Final fused images are attained using the inverse IHS algorithm. The proposed fusion algorithm is tested on two data sets of: a) panchromatic and multi spectral bands of IKONOS image with the same acquisition date, and b) multi spectral and HH bands of IKONOS and TerraSAR-X images respectively with different acquisition dates. Moreover, the obtained results are compared with other fusion methods like IHS, Gungor, Brovey and synthetic variable ratio. The results show less spectral discrepancy of the proposed method comparing to other methods. Finally, the outcome of proposed method is classified and classification overall accuracy is improved by 5.6 and 2 percentage for data set ‘a’ and ‘b’ respectively.     

基于小波纹理信息的星载SAR图像与TM图像的数据融合

遥感图像的数据融合是当前遥感界研究的热点问题之一。论述利用小波变换提取合成孔径雷达（SAR）图像的多尺度纹理信息，基于小波纹理信息将SAR图像与TM图像进行融合。选取徐州市南郊风景区的Radarsat卫星SAR图像和TM图像进行试验研究，并与颜色变换法融合图像进行对比分析，结果表明，无论是目视解译还是定量分析，该融合方法与颜色变换法相比，将获得更理想的高空间分辨率多光谱的融合图像。     

SPOT5影像数据不同融合方法的比较与评价

采用小波IHS变换、小波PCA变换、EHLERS变换等五种不同融合技术对SPOT5的多光谱影像和全色影像进行融合,并从均值、标准差等方面对融合结果进行了评价。研究表明：EHLERS融合后影像有很好的整体视觉效果,但在细节表达方面有所缺陷;改进后的IHS融合后影像清晰度较低;PCA变换融合容易导致影像失真;而小波IHS和小波PCA融合技术以数据保留量大、失真程度小、视觉效果好、影像层次分明等特点证明了小波分解技术的优势。