1,721,041 research outputs found
Hypercomplex Quality Assessment of Multi/Hyperspectral Images
No full textThis letter presents a novel image quality index which extends the Universal Image Quality Index for monochrome images to multispectral and hyperspectral images through hypercomplex numbers. The proposed index is based on the computation of the hypercomplex correlation coefficient between the reference and tested images, which jointly measures spectral and spatial distortions. Experimental results, both from true and simulated images, are presented on spaceborne and airborne visible/infrared images. The results prove accurate measurements of inter- and intraband distortions even when anomalous pixel values are concentrated on few bands
Panchromatic sharpening of remote sensing images using a multiscale Kalman filter
No full textThis paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and up-sampling the wavelet filter bank, and the vector multiscale Kalman filter, which is used to model the injection process of wavelet details. Kalman modelization is exploited by spatial detail analysis at coarser scales in which multispectral and panchromatic representations are known. Results are presented and discussed on very-high resolution images acquired by Quickbird satellite systems. Fusion simulations on spatially degraded data and fusion tests at the full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method
Integration of Landsat and SAR images based on intensity modulation
No full textThe paper presents a multisensor image fusion algorithm which extends the solutions proposed for pan-sharpening of multispectral (MS) data through intensity modulation, to the integration of SAR and multispectral imagery. The algorithm is based on the computation of the ratio between a speckle-filtered SAR image and a low-pass approximation, obtained by 'à-trous' wavelet decomposition, of the same filtered SAR image. This ratio modulates the intensity of the multispectral image, which is obtained by applying a linear transformation, i.e., a generalized IHS transform, to the original MS data. The modulated intensity image substitutes the original intensity image of the multispectral data and the inverse transform is applied to obtain the fused multispectral image. Experimental results are presented on Landsat ETM+ and ERS SAR images of an urban area. The results prove accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where information from SAR enhances the fused result which can be successfully applied both for display and classification purposes
Optimal MMSE Pan-Sharpening of Very High Resolution Multispectral Images
No full textIn this paper, we propose an optimum algorithm, in the minimum mean-square-error (mmse) sense, for panchromatic (Pan) sharpening of very high resolution multispectral (MS) images. The solution minimizes the squared error between the original MS image and the fusion result obtained by spatially enhancing a degraded version of the MS image through a degraded version, by the same scale factor, of the Pan image. The fusion result is also optimal at full scale under the assumption of invariance of the fusion parameters across spatial scales. The following two versions of the algorithm are presented: a local mmse (lmmse) solution and a fast implementation which globally optimizes the fusion parameters with a moderate performance loss with respect to the lmmse version. We show that the proposed method is computationally practical, even in the case of local optimization, and it outperforms the best state-of-the-art Pan-sharpening algorithms, as resulted from the IEEE Data Fusion Contest 2006, on true Ikonos and QuickBird data and on simulated Pléiades data
Weighted Least Squares Pan-Sharpening of Very High Resolution Multispectral Images
No full textThis paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with highresolution panchromatic observations. The proposed method exploits a Weighted Least Squares estimator to calculate injection parameters in the fusion model. For each pixel of the image a weight is calculated by a classification map. The classifier used in the experiments is a Support Vector Machine in order to obtain high accuracy on each land-cover type. Results are presented and discussed on very-high resolution images acquired by Quickbird and Ikonos satellite systems. Fusion simulations on spatially degraded data and fusion tests at full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method
Fusion of Panchromatic and Multispectral Images by Genetic Algorithms
No full textPan-sharpened MS is a fusion product in which the multispectral (MS) bands are spatially enhanced by the higher-resolution panchromatic (Pan) image. Most effective algorithms for pan-sharpening are based on multiresolution analysis (MRA), e.g., wavelets, Laplacian pyramids, wavelet frames, or curvelets. MRA approaches present one main critical point: filtering operations may produce ringing artifacts when high frequency details are extracted from the panchromatic image. In this paper, a pan-sharpening algorithm for 4-band MS data is proposed, which is not based on MRA, but it applies a Generalized Intensity-Hue-Saturation (GIHS) transformation to the MS bands. A genetic algorithm is adopted to define the injection model which establishes how the missing highpass information is extracted from the Pan image. The fitness function of the genetic algorithm which provides the algorithm parameters driving the fusion process is based on a quality index specifically designed for quality assessment of,4-band MS images. Both visual and objective comparisons with advanced fusion methods are presented on QuickBird image data
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