Rocco Restaino

A Critical Comparison Among Pansharpening Algorithms

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening

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.

A Pansharpening Method Based on the Sparse Representation of Injected Details

The application of sparse representation (SR) theory to the fusion of multispectral (MS) and panchromatic images is giving a large impulse to this topic, which is recast as a signal reconstruction problem from a reduced number of measurements. This letter presents an effective implementation of this technique, in which the application of SR is limited to the estimation of missing details that are injected in the available MS image to enhance its spatial features. We propose an algorithm exploiting the details self-similarity through the scales and compare it with classical and recent pansharpening methods, both at reduced and full resolution. Two different data sets, acquired by the WorldView-2 and IKONOS sensors, are employed for validation, achieving remarkable results in terms of spectral and spatial quality of the fused product.

Adaptive localization techniques in WiFi environments

Indoor localization of a mobile user can be performed by using the off-the-shelf 802.11 (WiFi) infrastructure. However most of the existing position estimators are based on a stationary environment assumption that turns out to be rarely true in practice. We analyze two different approaches for the simultaneous estimation of the position and of the signal statistical model. The first uses a discrete state approach and is based on the Expectation-Maximization (EM) algorithm; the second employs a continuous state space and Kalman or Particle Filtering methodology. Numerical simulations and implementation show the effectiveness of the latter for real-time applications in nonstationary environments.

Pansharpening Based on Semiblind Deconvolution

Many powerful pansharpening approaches exploit the functional relation between the fusion of PANchromatic (PAN) and MultiSpectral (MS) images. To this purpose, the modulation transfer function of the MS sensor is typically used, being easily approximated as a Gaussian filter whose analytic expression is fully specified by the sensor gain at the Nyquist frequency. However, this characterization is often inadequate in practice. In this paper, we develop an algorithm for estimating the relation between PAN and MS images directly from the available data through an efficient optimization procedure. The effectiveness of the approach is validated both on a reduced scale data set generated by degrading images acquired by the IKONOS sensor and on full-scale data consisting of images collected by the QuickBird sensor. In the first case, the proposed method achieves performances very similar to that of the algorithm that relies upon the full knowledge of the degrading filter. In the second, it is shown to outperform several very credited state-of-the-art approaches for the extraction of the details used in the current literature.

A Class of Cloud Detection Algorithms Based on a MAP-MRF Approach in Space and Time

A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms.

Fusion of Multispectral and Panchromatic Images Based on Morphological Operators

Nonlinear 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 gradient operators and demonstrate the suitability of this algorithm through the comparison with the state-of-the-art approaches. Four data sets 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.

MAP-MRF Cloud Detection Based on PHD Filtering

Temporal correlation has been recently taken into consideration to improve the performances of cloud detection algorithms. We exploit this concept within the Maximum A Posteriori Markov Random Field MAP-MRF framework by adding a penalization term which is determined according to the history of cloud masses. Multi Target Tracking of clouds is accomplished by methods of FInite Set Statistics (FISS) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.

Sequential Bayesian Methods for Resolution Enhancement of TIR Image Sequences

The availability of remotely sensed image sequences characterized by both spatial and temporal high resolution is crucial in many applications, ranging from agriculture to Earth surface hazard monitoring. To date, image sequences presenting such desirable characteristics in both domains are not directly obtainable by a single device and thus a viable solution is represented by the joint use of multisensor information. In this work, we propose a solution, based on Bayesian sequential estimation, for fusing two image sequences characterized by complementary features. Together with the assessment of two different sequential estimation approaches, a novel method for constructing a sharpened observations is presented here. The proposals are then evaluated by employing different datasets acquired by the SEVIRI and MODIS sensors, showing remarkable improvements with respect to classical approaches.

MultiResolution Analysis and Component Substitution techniques for hyperspectral Pansharpening

Images with high spatial and spectral resolutions are desirable for remote sensing applications. Unfortunately, due to sensor physical constraints, this result cannot be obtained by a single sensor. To overcome these limitations, a great number of data fusion approaches have been developed in the last years. The fusion of panchromatic and multispectral images, also known as Pansharpening, is capturing a lot of attention in the literature. In this paper, we extend and analyze the use of some classical pansharpening techniques, belonging to the MultiResolution Analysis and Component Substitution families, for fusing hyperspectral data instead of multispectral ones. The experimental results, conducted on two real datasets acquired by the Hyperion/ALI and CHRIS-Proba/QuickBird sensors, point out the greater suitability of the algorithms into the MRA class thanks to a better spectral consistency of the final products, which is a desirable feature when the number of bands to fuse increases.