Fusion of MultiSpectral and Panchromatic Images Based on Morphological Operators

International audienceNonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradients operators and demonstrate the suitability of this algorithm through the comparison with state of the art approaches. Four datasets acquired by the Pleiades, Worldview-2, Ikonos and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor

Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications

International audienceHyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For increasing the capabilities of the data in terms of utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by combining the hyperspectral image with a high spatial resolution panchromatic image. These techniques are generally known as pansharpening and can be divided into component substitution (CS) and multi-resolution analysis (MRA) based methods. In general, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. Both substitution and filtering approaches are considered adequate when applied to multispectral and PAN images, but have many drawbacks when the low-resolution image is a hyperspectral image. Thus, one of the main challenges in hyperspectral pansharpening is to improve the spatial resolution while preserving as much as possible of the original spectral information. An effective solution to these problems has been found in the use of hybrid approaches, combining the better spatial information of CS and the more accurate spectral information of MRA techniques. In general, in a hybrid approach a CS technique is used to project the original data into a low dimensionality space. Thus, the PAN image is fused with one or more features by means of MRA approach. Finally the inverse projection is used to obtain the enhanced image in the original data space. These methods, permit to effectively enhance the spatial resolution of the hyperspectral image without relevant spectral distortions and on the same time to reduce the computational load of the entire process. In particular, in this paper we focus our attention on the use of Non-linear Principal Component Analysis (NLPCA) for the projection of the image into a low dimensionality feature space. However, if on one hand the NLPCA has been proved to better represent the intrinsic information of hyperspectral images in the feature space, on the other hand, an analysis of the impact of different fusion techniques applied to the nonlinear principal components in order to define the optimal framework for the hybrid pansharpening has not been carried out yet. More in particular, in this paper we analyze the overall impact of several widely used MRA pansharpening algorithms applied in the nonlinear feature space. The results obtained on both synthetic and real data demonstrate that, an accurate selection of the pansharpening method can lead to an effective improvement of the enhanced hyperspectral image in terms of spectral quality and spatial consistency, as well as a strong reduction in the computational time

Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data

Multi-platform data introduce new possibilities in the context of data fusion, as they allow to exploit several remotely sensed images acquired by different combinations of sensors. This scenario is particularly interesting for the sharpening of hyperspectral (HS) images, due to the limited availability of high-resolution (HR) sensors mounted onboard of the same platform as that of the HS device. However, the differences in the acquisition geometry and the nonsimultaneity of this kind of observations introduce further difficulties whose effects have to be taken into account in the design of data fusion algorithms. In this study, we present the most widespread HS image sharpening techniques and assess their performances by testing them over real acquisitions taken by the Earth Observing-1 (EO-1) and the WorldView-3 (WV3) satellites. We also highlight the difficulties arising from the use of multi-platform data and, at the same time, the benefits achievable through this approach

A Study on Full Scale Injection Coefficients for Pansharpening

Pansharpening regards 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 estimation, at reduced resolution, of injection coefficients through regression is a widespread and powerful approach. In this work, the problem of the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is studied. Multiple approaches (based on guess images or an iterative method) are proposed. These are assessed at reduced resolution by exploiting a real dataset acquired by the IKONOS sensor. The quantitative results clearly demonstrate the superiority of the proposed iterative method

A Bayesian procedure for full-resolution quality assessment of pansharpened products

Pansharpening regards the fusion of a high-spatial resolution panchromatic image with a low-spatial resolution multispectral image. One of the most debated topics about pansharpening is related to the quality assessment of fused products. Two main assessment procedures are usually exploited in the literature: the reduced resolution validation and the full-resolution (FR) validation. The former has the advantage to be accurate, but the hypothesis of invariance among scales has to be assumed. The latter overcomes this limitation but paying it with a lower accuracy. In this paper, we will focus on the FR assessment proposing an approach for estimating an overall quality index at FR by using multiscale FR measurements. The problem is recast into the sequential Bayesian framework exploiting a Kalman filter to find its solution. The proposed procedure for quality evaluation has been tested on four real data sets acquired by the Pléiades, the GeoEye-1, the WorldView-3, and the WorldView-4 sensors assessing the quality of 19 pansharpened methods. The proposed approach has demonstrated its superiority with respect to the benchmark consisting of state-of-the-art quality assessment procedures

A Regression-Based High-Pass Modulation Pansharpening Approach

Pansharpening usually refers to the fusion of a high spatial resolution panchromatic (PAN) image with a higher spectral resolution but coarser spatial resolution multispectral (MS) image. Owing to the wide applicability of related products, the literature has been populated by many papers proposing several approaches and studies about this issue. Many solutions require a preliminary spectral matching phase wherein the PAN image is matched with the MS bands. In this paper, we propose and properly justify a new approach for performing this step, demonstrating that it yields state-of-the-art performance. The comparison with existing spectral matching procedures is performed by employing four data sets, concerning different kinds of landscapes, acquired by the Pléiades, WorldView-2, and GeoEye-1 sensors

A Study on Full Scale Injection Coefficients for Pansharpening

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