The Experts below are selected from a list of 6801 Experts worldwide ranked by ideXlab platform
Jocelyn Chanussot - One of the best experts on this subject based on the ideXlab platform.
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full scale regression based injection coefficients for Panchromatic sharpening
IEEE Transactions on Image Processing, 2018Co-Authors: Gemine Vivone, Rocco Restaino, Jocelyn ChanussotAbstract:Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (Panchromatic) Image with a high spectral resolution but low spatial resolution (multispectral) Image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.
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A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function
IEEE Transactions on Image Processing, 2018Co-Authors: Liang-jian Deng, Gemine Vivone, Mauro Dalla Mura, Jocelyn ChanussotAbstract:Pansharpening is an important application in remote sensing Image processing. It can increase the spatial-resolution of a multispectral Image by fusing it with a high spatial-resolution Panchromatic Image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc. In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a Panchromatic Image and a multispectral Image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction method of multipliers which guarantees the convergence of the proposed method. Extensive experiments on many real datasets collected by different sensors demonstrate the effectiveness of the proposed technique as compared with several state-of-the-art pansharpening approaches.
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Hyperspectral Pansharpening: A Review
IEEE geoscience and remote sensing magazine, 2016Co-Authors: Laetitia Loncan, Jocelyn Chanussot, Luís Almeida, José Bioucas-dias, Xavier Briottet, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel SimõesAbstract:Pansharpening aims at fusing a Panchromatic Image with a multispectral one, to generate an Image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral Images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.
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Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Gemine Vivone, Rocco Restaino, Mauro Dalla Mura, Giorgio Licciardi, Jocelyn ChanussotAbstract:The pansharpening process has the purpose of building a high-resolution multispectral Image by fusing low spatial resolution multispectral and high-resolution Panchromatic observations. A very credited method to pursue this goal relies upon the injection of details extracted from the Panchromatic Image into an upsampled version of the low-resolution multispectral Image. In this letter, we compare two different injection methodologies and motivate the superiority of contrast-based methods both by physical consideration and by numerical tests carried out on remotely sensed data acquired by IKONOS and Quickbird sensors.
Manfred Ehlers - One of the best experts on this subject based on the ideXlab platform.
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multi sensor Image fusion for pansharpening in remote sensing
International Journal of Image and Data Fusion, 2010Co-Authors: Manfred Ehlers, Sascha Klonus, Par Johan Astrand, Pablo RossoAbstract:The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a Panchromatic Image of high spatial resolution with multispectral Image data of lower spatial resolution to obtain a high-resolution multispectral Image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For Images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the Panchromatic Image and the spectral information from the multispectral Image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for Image fusion to show their ability to fuse multitemporal and multi-sensor Image data. A series of eight multitemp...
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fft enhanced ihs transform method for fusing high resolution satellite Images
Isprs Journal of Photogrammetry and Remote Sensing, 2007Co-Authors: Yangrong Ling, Manfred Ehlers, Lynn E Usery, Marguerite MaddenAbstract:Abstract Existing Image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite Images such as Ikonos and QuickBird. One problem is color distortion in the fused Image, which causes visual changes as well as spectral differences between the original and fused Images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite Images. This method combines a standard IHS transform with FFT filtering of both the Panchromatic Image and the intensity component of the original multispectral Image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information.
Bingkun Yin - One of the best experts on this subject based on the ideXlab platform.
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wavelet based remote sensing Image fusion with pca and feature product
International Conference on Mechatronics and Automation, 2006Co-Authors: Jian Liu, Jinwen Tian, Bingkun YinAbstract:Image fusion is one of the important techniques for Image information enhancing. In order to utilize respective information from different remote sensing Images, we propose a new Image fusion method based on the Principal Component Analysis (PCA) and feature product of wavelet transform. Firstly, the multi-spectral Image is transformed with PCA. Secondly, the histogram-matched Panchromatic Image and the first principal component are decomposed into wavelet coefficients respectively. Thirdly, the first principal component of the multi-spectral Image and the Panchromatic Image are merged with feature product of wavelet based fusion method, and the former is replaced with the merged data. Finally, the new multi-spectral Image is obtained by inverse PCA. Some evaluation parameters are suggested and applied to compare the new method with those of PCA method, the combined PCA and traditional wavelet method and the combined PCA and local deviation of wavelet method. Subjective visual effect and objective statistical results indicate that the performance of the new method is better than those methods. It not only preserves spectral information of the original multi-spectral Image well, but also enhances spatial detail information greatly.
Zhenwei Shi - One of the best experts on this subject based on the ideXlab platform.
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Panchromatic Image processing using hyperspectral unmixing method
International Geoscience and Remote Sensing Symposium, 2015Co-Authors: Zhenwei Shi, Hongqiang WangAbstract:In the paper, we consider the probability of applying hyper-spectral Image (HSI) processing methods to Panchromatic Images (PIs), which is a novel yet crucial issue for further analyses. To achieve the purpose, we propose an effective approach for handling PI with HSI unmixing methods. In the approach, HSI simulating process is first implemented to obtain a synthetic HSI from PI. After that, a hyperspectral unmixing algorithm, vertex component analysis, is then applied to extract endmembers that comprise the vertices of the data simplex. Meanwhile, we calculate abundances of HSI by employing least squares method. We will see that the unmixing results, namely endmembers and abundances, can be used for target detection and other applications. Different objects such as ships and cars were successfully extracted from the backgrounds, which demonstrates the efficacy of the proposed approach.
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an automated airplane detection system for large Panchromatic Image with high spatial resolution
Optik, 2014Co-Authors: Zhenwei Shi, Xichao Teng, Wei TangAbstract:Abstract With a wide range of applications in different fields like airport management and military warfare, airplane detection has been a critical part in remote sensing Image processing. In this paper, we focus on the airplane detection in large (usually larger than 10,000 × 10,000 pixels) Panchromatic Image (PI) with high spatial resolution (usually superior to 1 m), and propose an automated airplane detection system. The system contains two main modules: In the first module, line segment detector (LSD) is applied to detect runway of an airport, thus segmenting airport region in a large PI and reducing airplane candidates. The other is used to detect airplanes in the segmented airport regions. We first use circle frequency filter to further locating airplane candidates, then accomplish precise detection task by combining Histograms of Oriented Gradients (HOG) descriptor and AdaBoost algorithm. Therefore, besides a practical airplane detection system, the other contributions of our approach include the following three parts: (1) it locates runway of an airport with LSD; (2) it classifies airplane candidates by using circle frequency filter; (3) it precisely detects airplanes by exploiting HOG and AdaBoost algorithm. Experimental results on real data indicate the efficacy of the proposed system. The airport and airplane detection rates are higher than 94% and 96%, respectively. Meanwhile, the false alarm rate of airplane detection is superior to 0.05%. Moreover, the whole time cost for handling a large PI is less than 2.5 min, which implies that the system is a satisfactory choice for airplane detection in practical applications.
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nonnegative matrix factorization based hyperspectral and Panchromatic Image fusion
Neural Computing and Applications, 2013Co-Authors: Zhou Zhang, Zhenwei ShiAbstract:The fusion of hyperspectral Image and Panchromatic Image is an effective process to obtain an Image with both high spatial and spectral resolutions. However, the spectral property stored in the original hyperspectral Image is often distorted when using the class of traditional fusion techniques. Therefore, in this paper, we show how explicitly incorporating the notion of “spectra preservation” to improve the spectral resolution of the fused Image. First, a new fusion model, spectral preservation based on nonnegative matrix factorization (SPNMF), is developed. Additionally, a multiplicative algorithm aiming at get the numerical solution of the proposed model is presented. Finally, experiments using synthetic and real data demonstrate the SPNMF is a superior fusion technique for it could improve the spatial resolutions of hyperspectral Images with their spectral properties reliably preserved.
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an airplane detection method for Panchromatic Image
International Conference on Signal and Information Processing, 2013Co-Authors: Zhenwei ShiAbstract:Airplane detection is one of the crucial parts in the field of remote sensing Image processing. In this paper, we focus on airplane detection in the remote sensing Panchromatic Images (PIs) with high spatial resolution (usually superior to 2m). An automated airplane detection system is proposed in the paper. The system contains two main modules: in module one, we use the circle frequency filter to quickly locate the candidates of airplanes; in module two, the precise detection process is accomplished by combining reconstruction independence component analysis (RICA) and SVM algorithm. Experimental results from the real data indicate that the system is effective in handling the remote sensing Images. Therefore, it is a good choice for airplane detection in PIs.
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hyperspectral and Panchromatic Image fusion using unmixing based constrained nonnegative matrix factorization
Optik, 2013Co-Authors: Zhou Zhang, Zhenwei ShiAbstract:Abstract Image fusion is an important technique in remote sensing, as it could effectively combine the high spatial and the high spectral resolutions in order to obtain the complete and accurate description of the observed scene. To date, many Image fusion techniques have been developed. However, the available methods could hardly produce the satisfactory results in dealing with the fusion between the hyperspectral Image and Panchromatic Image, especially in the spectral aspect. Therefore, in this paper, a new fusion approach, called unmixing-based constrained nonnegative matrix factorization (UCNMF), is proposed. This approach uses the NMF unmixing technique to generate the abundance matrix and uses the Panchromatic Image to sharpen the the material maps. The constrained term aiming at preserving the spectral information is added and the fusion problem is turned into a constrained optimization problem. Additionally, a projected gradient algorithm aiming at get the numerical solution of the optimization problem is presented. Finally, three groups of experiments are given to demonstrate that the proposed fusion method could be recognized as an effective technique in hyperspectral Image fusion.
Pablo Rosso - One of the best experts on this subject based on the ideXlab platform.
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multi sensor Image fusion for pansharpening in remote sensing
International Journal of Image and Data Fusion, 2010Co-Authors: Manfred Ehlers, Sascha Klonus, Par Johan Astrand, Pablo RossoAbstract:The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a Panchromatic Image of high spatial resolution with multispectral Image data of lower spatial resolution to obtain a high-resolution multispectral Image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For Images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the Panchromatic Image and the spectral information from the multispectral Image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for Image fusion to show their ability to fuse multitemporal and multi-sensor Image data. A series of eight multitemp...
