Gemine Vivone

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.

Hyperspectral Pansharpening: A Review

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 literatures 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.

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.

Knowledge-Based Multitarget Ship Tracking for HF Surface Wave Radar Systems

These last decades spawned a great interest toward low-power high-frequency (HF) surface-wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early warning tools in maritime situational awareness applications providing an additional source of information for target detection and tracking. Unfortunately, they also exhibit many shortcomings that need to be taken into account, and proper algorithms need to be exploited to overcome their limitations. In this paper, we develop a knowledge-based (KB) multitarget tracking methodology that takes advantage of a priori information on the ship traffic. This a priori information is given by the ship sea lanes and by their related motion models, which together constitute the basic building blocks of a variable structure interactive multiple model procedure. False alarms and missed detections are dealt with using a joint probabilistic data association rule and nonlinearities are handled by means of the unscented Kalman filter. The KB-tracking procedure is validated using real data acquired during an HF-radar experiment in the Ligurian Sea (Mediterranean Sea). Two HFSW radar systems were operated to develop and test target detection and tracking algorithms. The overall performance is defined in terms of time-on-target, false-alarm rate (FAR), track fragmentation (TF), and accuracy. A full statistical characterization is provided using one month of data. A significant improvement of the KB-tracking procedure, in terms of system performance, is demonstrated in comparison with a standard joint probabilistic data association tracker recently proposed in the literature to track HFSW radar data. The main improvement of our approach is the better capability of following targets without increasing the FAR. This increment is much more evident in the region of low FAR, where it can be over the 30% for both the HFSW radar systems. The KB-tracking exhibits on average a reduction of the TF of about the 20% and the 13% of the utilized HFSW-radar systems.

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.

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.