Filippo Nencini

A global quality measurement of pan-sharpened multispectral imagery

This letter focuses on quality assessment of fusion of multispectral (MS) images with high-resolution panchromatic (Pan) observations. A new quality index suitable for MS imagery having four spectral bands is defined from the theory of hypercomplex numbers, or quaternions. Both spectral and radiometric distortion measurements are encapsulated in a unique measurement, simultaneously accounting for local mean bias, changes in contrast, and loss of correlation of individual bands, together with spectral distortion. Results are presented and discussed on very high-resolution QuickBird data, through comparisons between state-of-the-art and advanced MS+Pan merge algorithms.

Remote sensing image fusion using the curvelet transform

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS+Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.

Multispectral and Panchromatic Data Fusion Assessment Without Reference

This paper introduces a novel approach for evaluating the quality of pansharpened multispectral (MS) imagery without resorting to reference originals. Hence, evaluations are feasible at the highest spatial resolution of the panchromatic (PAN) sensor. Wang and Bovik’s image quality index (QI) provides a statistical similarity measurement between two monochrome images. The QI values between any couple of MS bands are calculated before and after fusion and used to define a measurement of spectral distortion. Analogously, QI values between each MS band and the PAN image are calculated before and after fusion to yield a measurement of spatial distortion. The rationale is that such QI values should be unchanged after fusion, i.e., when the spectral information is translated from the coarse scale of the MS data to the fine scale of the PAN image. Experimental results, carried out on very high-resolution Ikonos data and simulated Pleiades data, demonstrate that the results provided by the proposed approach are consistent and in trend with analysis performed on spatially degraded data. However, the proposed method requires no reference originals and is therefore usable in all practical cases.

Optimal MMSE Pan Sharpening of Very High Resolution Multispectral Images

In 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 Pleiades data.

Error-Resilient and Low-Complexity Onboard Lossless Compression of Hyperspectral Images by Means of Distributed Source Coding

In this paper, we propose a lossless compression algorithm for hyperspectral images inspired by the distributed-source-coding (DSC) principle. DSC refers to separate compression and joint decoding of correlated sources, which are taken as adjacent bands of a hyperspectral image. This concept is used to design a compression scheme that provides error resilience, very low complexity, and good compression performance. These features are obtained employing scalar coset codes to encode the current band at a rate that depends on its correlation with the previous band, without encoding the prediction error. Iterative decoding employs the decoded version of the previous band as side information and uses a cyclic redundancy code to verify correct reconstruction. We develop three algorithms based on this paradigm, which provide different tradeoffs between compression performance, error resilience, and complexity. Their performance is evaluated on raw and calibrated AVIRIS images and compared with several existing algorithms. Preliminary results of a field-programmable gate array implementation are also provided, which show that the proposed algorithms can sustain an extremely high throughput.

Hypercomplex Quality Assessment of Multi/Hyperspectral Images

This 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.

Analysis of the Effects of Pansharpening in Change Detection on VHR Images

In this letter, we investigate the effects of pansharpening (PS) applied to multispectral (MS) multitemporal images in change-detection (CD) applications. Although CD maps computed from pansharpened data show an enhanced spatial resolution, they can suffer from errors due to artifacts induced by the fusion process. The rationale of our analysis consists in understanding to which extent such artifacts can affect spatially enhanced CD maps. To this end, a quantitative analysis is performed which is based on a novel strategy that exploits similarity measures to rank PS methods according to their impact on CD performance. Many multiresolution fusion algorithms are considered, and CD results obtained from original MS and from spatially enhanced data are compared.

Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

This 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.

PAN‐sharpening of very high resolution multispectral images using genetic algorithms

A novel image fusion method is presented, suitable for sharpening of multispectral (MS) images by means of a panchromatic (PAN) observation. The method is based on redundant multiresolution analysis (MRA); the MS bands expanded to the finer scale of the PAN band are sharpened by adding the spatial details from the MRA representation of the PAN data. As a direct, unconditioned injection of PAN details gives unsatisfactory results, a new injection model is proposed that provides the optimum injection by maximizing a global quality index of the fused product. To this aim, a real‐valued genetic algorithm (GA) has been defined and tested on Quickbird data. The optimum GA injection is driven by an index function capable of measuring different types of possible distortions in the fused images. Fusion tests are carried out on spatially degraded data to objectively compare the proposed scheme to the most promising state‐of‐the‐art image fusion methods, and on full‐resolution image data to visually assess the performance of the proposed genetic image fusion method.

Fusion of Panchromatic and Multispectral Images by Genetic Algorithms

Pan-sharpened MS is a fusion product in which the multispectral (MS) bands are spatially enhanced by the higher-resolution panchromatic (Pan) image. Most effective algo- rithms 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. I. INTRODUCTION Multispectral (MS) observations from spaceborne imaging sensors exhibit ground resolutions that may be inadequate to specific identification tasks, especially when urban areas are concerned. Data merge methods, based on injecting spatial details taken from a panchromatic image (Pan) into resampled versions of the MS data, have demonstrated superior performances. In the last years, multiresolution analysis (MRA), based on wavelets, Laplacian pyramids, wavelet frames, curvelets, etc., has been applied to produce effective tools to help carry out data fusion/merge tasks. However, MRA approaches present one main critical point: filtering operations may produce ringing artifacts, e.g. when high frequency details are extracted from the panchromatic image. This problem does not decrease significantly any global quality index, but it may locally reduce the visual quality of the fused product in a considerable way. To avoid this problem, we propose a pan-sharpening algorithm which is not based on MRA, but it applies a Generalized Intensity-Hue-Saturation (GIHS) transformation to the MS bands and makes use of optimal parameters which are computed by a genetic algorithm. Similarly to other pan-sharpening methods based on the injection of spatial details, the proposed algorithm assumes an injection model. To overcome instability and data-dependent results which are typical of space-varying models, we propose a simple injection model in which the coefficients that equalize the Pan image before detail injection into the MS image are derived globally - one for each band - from coarser scales, similarly to previous schemes such as SDM (1), CBD (2) and RWM (3) techniques, but not a-priori defined on image statistics, e.g., variance, mean, correlation coefficient, etc. The coefficients are computed by a genetic algorithm (GA) together with the weights which define the generalized intensity of the original MS bands. The genetic algorithm adopts the Q4 quality index defined in (4) as the fitness function to be maximized for optimal fusion parameter estimation.