High Spatial Resolution Panchromatic Image Research Articles

A Review of Quality Metrics for Fused Image

Image fusion is the process of combining high spatial resolution panchromatic (PAN) image and rich multispectral (MS) image into a single image. The fused single image obtained is known to be spatially and spectrally enhanced compared to the raw input images. In recent years, many image fusion techniques such as principal component analysis, intensity hue saturation, brovey transforms and multi-scale transforms, etc., have been proposed to fuse the PAN and MS images effectively. However, it is important to assess the quality of the fused image before using it for various applications of remote sensing. In order to evaluate the quality of the fused image, many researchers have proposed different quality metrics in terms of both qualitative and quantitative analyses. Qualitative analysis determines the performance of the fused image by visual comparison between the fused image and raw input images. On the other hand, quantitative analysis determines the performance of the fused image by two variants such as with reference image and without reference image. When the reference image is available, the performance of fused image is evaluated using the metrics such as root mean square error, mean bias, mutual information, etc. When the reference image is not available the performance of fused image is evaluated using the metrics such as standard deviation, entropy, etc. The paper reviews the various quality metrics available in the literature, for assessing the quality of fused image.

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.

The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: a) a low spatial resolution multispectral image; and b) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain we encourage low-rank structure, whereas in the spatial domain we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and the fused multispectral images. A weighted version of the vector Total Variation (TV) norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed Split Augmented Lagrangian Shrinkage (SALSA) algorithm to effectively solve the proposed variational formulations. Experimental results on simulated and real remote sensing images show the effectiveness of the proposed pansharpening method compared to the state-of-the-art.

Pan-sharpening based on Merged Product of Spatial Frequency Components of PAN and Intensity Images

Image fusion or Pan Sharpening produces high quality fused multispectral image using high spatial resolution Panchromatic (PAN) image and low spatial resolution multispectral (MS) image. The aim is to retain to a large extent both the high frequency spatial component of PAN and high spectral resolution of MS data. There are many approaches (more than 10) for image fusion based on different colour models and spatial transformations, the performances of some of which using the IKONOS-2 PAN and MS images have been evaluated earlier by the authors of this paper. In this paper, the concept of combining different spatial frequency components of the PAN and intensity images and merging with hue and saturation components is attempted on all the 10 approaches of image fusion. The technique of merging of high spatial frequency of PAN and low spatial frequency component of intensity images is described. The results of the statistical analysis show that there is marked improvement in retention of the spectral information albeit at slight loss of spatial correlation in pansharpening. The high level of retention of spectral information is especially significant in view of expected better classification accuracy.

Multiresolution Image Fusion: Use of Compressive Sensing and Graph Cuts

In this paper, we propose a new approach for multiresolution fusion, i.e., obtaining a high spatial and spectral resolution multispectral (MS) image using the available low spatial resolution MS and the high spatial resolution Panchromatic (Pan) image. Our approach is based on the idea of compressive sensing (CS) and graph cuts. Assuming that both the MS and Pan images have the same sparseness, a close approximation to the MS image is obtained from the Pan image using the theory of compressive sensing and l1 minimization. We then use regularization framework to obtain fused image. The low resolution (LR) MS image is modeled as degraded and noisy version of fused image in which degradation matrix entires estimated using the close approximation are used. The regularization is carried out by using truncated quadratic smoothness prior which takes care of preservation of the discontinuities in the fused image. A suitable energy function is then formed consisting of data fitting term and prior term. Minimization of the energy function is carried out using a computationally efficient graph cuts optimization to obtain final fused image. Advantage of our approach is that the Pan and MS images need not be registered. This is because, we are not directly using the Pan digital numbers to derive the fused image. The effectiveness of the proposed method is illustrated by conducting experiments on real satellite images. Subjective and quantitative comparison of the proposed method with the state-of-the-art approaches indicates efficacy of our approach.

A Fast Level Set Algorithm for Building Roof Recognition From High Spatial Resolution Panchromatic Images

Traditional level set methods usually require repeated tuning of parameters, which is quite laborious and thus limits their applications. In order to simplify the parameter setting, this letter presents a fast level set algorithm that is a further extension of the original Chan-Vese model. For computational efficiency, we start by initializing the level set function in our algorithm as a binary step function rather than the often used signed distance function. Then, we eliminate the curvature-based regularizing term that is commonly used in traditional models. Thus, we can use a relatively larger time step in the numerical scheme to expedite our model. Furthermore, to keep the evolving level curves smooth, we introduce a Gaussian kernel into our algorithm to convolve the updated level set function directly. Finally, compared with other existing popular algorithms in an experiment of recognizing building roofs from high spatial resolution panchromatic images, the proposed model is much more computationally efficient while object recognition performance is comparable to other popular models.

An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion

Most earth observation satellites, such as IKONOS, QuickBird, GeoEye, and WorldView-2, provide a high spatial resolution (HR) panchromatic (Pan) image and a multispectral (MS) image at a lower spatial resolution (LR). Image fusion is an effective way to acquire the HR MS images that are widely used in various applications. In this paper, we propose an online coupled dictionary learning (OCDL) approach for image fusion, in which a superposition strategy is applied to construct the coupled dictionaries. The constructed coupled dictionaries are further developed via an iterative update to ensure that the HR MS image patch can be almost identically reconstructed by multiplying the HR dictionary and the sparse coefficient vector, which is solved by sparsely representing its counterpart LR MS image patch over the LR dictionary. The fusion results from IKONOS and WorldView-2 data show that the proposed fusion method is competitive or even superior to the other state-of-the-art fusion methods.

Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images

Commercial multispectral satellite datasets, such as WorldView-2 and Geoeye-1 images, are often delivered with a high-spatial resolution panchromatic image (PAN) as well as a corresponding lower resolution multispectral image (MSI). Certain fine features are only visible on the PAN but are difficult to discern on the MSI. To fully utilize the high-spatial resolution of the PAN and the rich spectral information from the MSI, a pan-sharpening process can be carried out. However, difficulties arise in maintaining radiometric accuracy, particularly for applications other than visual assessment. We propose a fast pan-sharpening process based on nearest-neighbor diffusion with the aim to enhance the salient spatial features while preserving spectral fidelity. Our approach assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring superpixels; it treats each spectrum as its smallest element of operation, which is different from the most existing algorithms that process each band separately. Our approach is shown to be capable of preserving salient spatial and spectral features. We expect this algorithm to facilitate fine feature extraction from satellite images.

Assessment of pan-sharpened very high-resolution WorldView-2 images

The fusion of multispectral (MS) images with high spatial resolution panchromatic (pan) images compensates the trade-off between spatial and spectral resolutions. The performance of fusion algorithms for different sensors is an active area of research. Therefore, with the availability of new very high-resolution (VHR) sensors, it becomes customary to evaluate the applicability of existing fusion techniques. In this study, we focused on the WorldView-2 (WV2) spaceborne sensor, which captures data in eight MS (2 m spatial resolution) bands and one pan (0.5 m spatial resolution) band. We compared and assessed 12 fusion techniques, namely Brovey transform (BT), Ehlers, Gram–Schimdt (GS), hyperspherical colour sphere (HCS), high-pass filter (HPF), modified intensity hue saturation (ModIHS), Multiplicative, PANSHARP, PANSHARP2, principal component (PC), and wavelet (IHS and PC)-based methods. To measure the quality of fused products, qualitative and quantitative methods were used. In qualitative methods, visual analysis of different colour composites was carried out. In quantitative methods, band-wise eight quality metrics including the required processing time and controlling parameters were reported. For overall image quality assessment, it is necessary to combine the values of different quality metrics. Therefore, a mean observation score based on these values was calculated to rank the fusion methods. It was observed that the HPF and PANSHARP methods produced the most visually appealing images, whereas quantitative values indicated that HCS, HPF, and PANSHARP methods performed better than other methods. Combining the results of quantitative and qualitative analyses, it was found that PANSHARP and HPF methods are superior to other methods in preserving both spatial and spectral details.

A comparative study of various pixel-based image fusion techniques as applied to an urban environment

In this paper, the use of high-resolution images for identification of urban features through pixel-based image fusion techniques is discussed. Fusion techniques are used to merge high spatial resolution panchromatic (PAN) image with low spatial resolution multispectral (MS) image to enhance the visual quality/appearance of some of the urban features present in the image. In this paper nine pixel-based fusion techniques, viz., Principal Component Analysis (PCA), multiplicative, Brovey transformation, wavelet analysis, subtractive, HPF, modified IHS, Ehlers, and hyperspherical color space, are discussed for the fusion of PAN and MS IKONOS images. The fused images are interpreted on the basis of visual comparison, correlation coefficients and histogram statistics. All the nine fusion techniques improved the resolution and the visual appearance of the original MS image. These are comparable to the resolution of the original PAN image. All the three resampling methods (nearest neighborhood, bilinear interpolation and the cubic convolution) do not have any significant effect on the final visual appearance of the fused images. All fusion techniques result in a change of the statistical parameters of the original images. The multiplicative technique results in major changes of the statistical parameters than the other techniques. After doing visual comparison and analyzing the statistical parameters, the Brovey and the PCA techniques seem to gather more information of urban features in fused PAN and MS IKONOS images.

High spatial resolution panchromatic remote sensing image simulation

PDF HTML阅读 XML下载 导出引用 引用提醒 基于艇载遥感成像数据的高空间分辨率全色遥感图像仿真 DOI: 作者: 作者单位: 1：中国科学院安徽光学精密机械研究所 通用光学定标与表征技术重点实验室;2：解放军电子工程学院,中国科学院安徽光学精密机械研究所 通用光学定标与表征技术重点实验室,中国科学院安徽光学精密机械研究所 通用光学定标与表征技术重点实验室,中国科学院安徽光学精密机械研究所 通用光学定标与表征技术重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（面上项目，重点项目，重大项目） High spatial resolution panchromatic remote sensing image simulation Author: Affiliation: 1.Key laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics of Chinese Academy of Sciences; 2：Electronic Engineering Institute of PLA,Key laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics of Chinese Academy of Sciences,Key laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics of Chinese Academy of Sciences,Key laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics of Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:高空间分辨率全色遥感图像在军事侦察、地面监视等领域具有较高的应用价值.为模拟星载全色遥感图像, 提出了一种由艇载遥感成像系统获取的低空遥感图像为数据源的高空间分辨率全色遥感图像仿真方法.首先将低空宽视场图像按典型地物类型进行监督分类, 其次将低空宽视场图像与多光谱图像按不同地物类型分类拟合, 并将多光谱拟合结果合成高空间分辨率全色仿真图像, 最后对高空间分辨率全色仿真图像进行仿真精度评价.相比星载全色遥感图像, 仿真图像同样具备高空间分辨率、全色波段、宽视场等特点.仿真方法可为星载全色遥感图像仿真提供较准确的数据支撑. Abstract:Panchromatic remote sensing image with high spatial resolution has been applied in military reconnaissance, ground surveillance and other areas. A simulation method for high spatial resolution panchromatic remote sensing image is presented .The data source of the simulation is a kind of the airship remote sensing image. The low-altitude wide-field images were classified according to typical surface features. Then these images and the multi-spectral images were fitted to different typical features. The high spatial resolution panchromatic simulation image was composed by the fitting results. The simulation precision was assessed. Compared with satellite panchromatic remote sensing images, high spatial resolution panchromatic simulation image has the same spatial resolution, panchromatic band, wide view and so on. This method provides accurate data source for the satellite panchromatic remote sensing image simulation. 参考文献 相似文献 引证文献

Survey on Different Image Fusion Techniques

In medical imaging and remote sensing, image fusion technique is a useful tool used to fuse high spatial resolution panchromatic images (PAN) with lower spatial resolution multispectral images (MS) to create a high spatial resolution multispectral of image fusion while preserving the spectral information in the multispectral image (MS).Image fusion is the process that combines information from multiple images of the samescene. The result of image fusion is a new image that retains the most desirable informationand characteristics of each input image. Now-a-days, almost all areas of medical diagnosis are impacted by the digital image processing. When an image is processed for visual interpretation, the human eye is the judge of how well a particular method works. Clinical application demanding Radiotherapy plan, for instance, often benefits from the complementary information in images of different modalities. For medical diagnosis, Magnetic Resonance Image (MRI) is a medical imaging technique used in radiology to visualize internal structures of the body in detail. MRI provides better information on soft tissue with more distortion. Whereas, Computed Tomography (CT) provides the best information on denser tissue with less distortion. Wavelet transform fusion is more formally defined by considering the wavelet transforms of the two registered input images together with the fusion rule .Then, the inverse wavelet transform is computed, and the fused image is reconstructed. The wavelets used in image fusion can be classified into three categories Orthogonal, Bi-orthogonal and A'trous' wavelet. Although these wavelets share some common properties, each wavelet has a unique image decompression and reconstruction characteristics that lead to different fusion results. Since medical images have several objects and curved shapes, it is expected that the curvelet transform would be better in their fusion. In this paper the fusion results are compared visually and statistically. The simulation results show the superiority of the curvelet transform to the wavelet transform in the fusion of digital image and MR and CT images from entropy, difference entropy, quality measure, standard deviation, PSNR.

Adjustable Model-Based Fusion Method for Multispectral and Panchromatic Images

In this paper, an adjustable model-based image fusion method for multispectral (MS) and panchromatic (PAN) images is developed. The relationships of the desired high spatial resolution (HR) MS images to the observed low-spatial-resolution MS images and HR PAN image are formulated with image observation models. The maximum a posteriori framework is employed to describe the inverse problem of image fusion. By choosing particular probability density functions, the fused HR MS images are solved using a gradient descent algorithm. In particular, two functions are defined to adaptively determine most regularization parameters using the partially fused results at each iteration, retaining one parameter to adjust the tradeoff between the enhancement of spatial information and the maintenance of spectral information. The proposed method has been tested using QuickBird and IKONOS images and compared to several known fusion methods using quantitative evaluation indices. The experimental results verify the efficacy of this method.

An efficient PAN sharpening technique by merging two hybrid approaches

To improve the spatial quality in multispectral satellite images by injecting high spatial details from the panchromatic image using two hybrid techniques namely local variation+contourlet transform(local variation+CT) and Adaptive Principal Component Analysis +Contourlet transform(APCA+CT). This process of injecting information from the high spatial resolution panchromatic image into the low spatial resolution multispectral image to get a high spatial and spectral quality satellite image is known as Pan-Sharpening. The final pan-sharpened image is required to retain the natural color of the multi-spectral input image with minimum spectral distortion and enhanced spatial details of the panchromatic image. The proposed technique by merging two hybrid approaches performs better than the existing techniques. The performance of this proposed fusion technique is highlighted by comparing with conventional techniques like PCA, PCA+CT, APCA, APCA+CT in terms of ERGAS and universal image quality index, Q.

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.

AN ARITHMETIC RS IMAGE FUSION METHOD FOR SPECTRAL PRESERVATION:HIGH PASS FILTER FUSION

The fusion methods such as IHS transform could merge two sorts of optical image data with different resolutions-a high spatial resolution panchromatic image and a low spatial resolution multispectral image. Nevertheless, such fusion methods require that the spectral response range of the high spatial resolution panchromatic image is identical or approximates to the spectral response range of the multispectral image. This paper puts forward a new fusion method called high-pass filter fusion (HPFF) that could merge two sorts of optical image data with different spectral characteristics. Its algorithm is as follows: First, to filter on the panchromatic image with high pass filter, and then to fuse the remote sensing data by applying IHS transformation. The color of the HPFF image approximates to the color of multispectral image. The HPFF method is more effective in spectral preservation than the IHS transformation.

Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN’s spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process.

Vegetation extraction from IKONOS imagery using high spatial resolution index

In vegetation change monitoring and urban planning, the measurement and mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular measure to generate vegetation maps which resolution depends on that of the input images. High-resolution imagery can lead to better vegetation classification accuracy. Various methods are proposed to provide high spatial resolution vegetation indices based on a fusion concept. IKONOS produces high spatial resolution panchromatic (Pan) images and moderate spatial resolution multispectral (MS) images. Generally, for an image fusion purpose, the conventional bi-cubic interpolation scheme is used to resize MS images. Nevertheless, this scheme fails around edges and consequently produces blurred edges and annoying artifacts in interpolated MS images. To avoid this problem, an artifact-free image interpolation method is proposed. This study presents a modified NDVI that provides high spatial resolution maps which differentiate vegetated surfaces from other surfaces when using IKONOS imagery. This vegetation index (HRNDVI: high resolution NDVI) is based on a newly derived formula including high spatial resolution information from IKONOS. The HRNDVI is computed based on the resampled MS images and the Pan images. The proposed vegetation index takes advantage of both the high spatial resolution information of Pan images and the robustness of the interpolation technique. Visual and quantitative analysis demonstrates that this index appears promising and performs well in vegetation extraction and visualization.

Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis

Image fusion is one of the most commonly used image enhancement techniques for improving the spatial quality of the source image with minimal spectral distortion in remote sensing. Until now, data fusion algorithms were developed and applied to improve the spatial resolution of the multispectral images and also their performances were evaluated depending on the source images such as Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Multispectral (MS)/Panchromatic (PAN), Satellite pour l'Observation de la Terre (SPOT) XS/PAN and IKONOS MS/PAN datasets. This paper assesses whether hyperspectral images, having very narrow bands compared to multispectral images, can be fused with high spatial resolution panchromatic images using common and current new algorithms including Intensity‐Hue Saturation (IHS), Principal Component Substitution (PCS), Gram Schmidt Transformation (GST), Smoothing Filter‐based Intensity Modulation (SFIM), Discrete Wavelet Transform (DWT), wavelet‐based IHS (DWT‐IMS) and PCS (DWT‐PCS) and Fast Fourier Transform (FFT)‐enhanced IHS. We also examine the performance of the fused hyperspectral images with respect to the fused multispectral images. For this purpose, two different source datasets (EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN) were used. Some qualitative and quantitative analyses were implemented to assess the spatial and spectral quality of the fused images. The results show that it was possible to carry out the fusion of a narrow‐band hyperspectral image and a high spatial resolution panchromatic image. The fusion of EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN datasets using the SFIM, DWT‐PCS, DWT‐IHS and FFT‐IHS algorithms produces better results than other techniques. Also, the results show that the fusion methods behaved for both datasets in the same performances except the DWT algorithm. The DWT method has a lower performance for the hyperspectral image compared to the multispectral image. Therefore the DWT algorithm should be further studied to improve the spectral qualities of a fused hyperspectral image based on wavelet transformation.

ISVR: an improved synthetic variable ratio method for image fusion

An Improved Synthetic Variable Ratio (ISVR) fusion method is proposed to merge high spatial resolution panchromatic (Pan) images and multispectral (MS) images based on a simulation of the panchromatic image from the multispectral bands. Compared to the existing SVR (Synthetic Variable Ratio) family methods, the ISVR method manifests two major improvements: a simplified and physically meaningful scheme to derive the parameters necessary as required by SVR, and less computing power. Two sets of IKONOS Pan and MS images: one in urban area and another one in a forest area, were used to evaluate the effectiveness of classification-oriented ISVR method in comparison to the Principal Component Substitution (PCS), Synthetic Variable Ratio (SVR) and Gram-Schmidt Spectral Sharpening (GS) methods that are available in the ENVI software package. Results indicate the ISVR method achieves the best spectral fidelity to facilitate classification compared to PCS, SVR, and GS methods.

Measuring coseismic deformation on the northern segment of the Bam-Baravat escarpment associated with the 2003 Bam (Iran) earthquake, by correlation of very-high-resolution satellite imagery

SUMMARY The role that the oblique reverse fault projecting to the surface at the Bam-Baravat escarpment (BBE) played in the slip accommodation of the 2003 (Mw 6.5) Bam earthquake is still unclear, regardless of many seismological and geodetic studies following this event. In this study, we correlate pre- and post-seismic very high spatial resolution panchromatic satellite images to map coseismic surface deformation along the northern segment of the BBE, a few hundred meters east of the urban area of Bam. Using a new approach based on principal component analysis (PCA) on offset measurements, we obtain 1.8 ± 0.6 cm east slip component and 2–6 ± 0.6 cm south slip component along the fault segment. Our results are consistent with ground observations over the study area and support the idea of the reactivation of the shallow part of this fault segment.