A review of image fusion algorithms based on the Super-Resolution paradigm

A critical analysis of remote sensing image fusion methods based on the super-resolution (SR) paradigm is presented in this paper. Very recent algorithms have been selected among the pioneering studies adopting a new methodology and the most promising solutions. After introducing the concept of super-resolution and modeling the approach as a constrained optimization problem, different SR solutions for spatio-temporal fusion and pan-sharpening are reviewed and critically discussed. Concerning pan-sharpening, the well-known, simple, yet effective, proportional additive wavelet in the luminance component (AWLP) is adopted as a benchmark to assess the performance of the new SR-based pan-sharpening methods. The widespread quality indexes computed at degraded resolution, with the original multispectral image used as the reference, i.e., SAM (Spectral Angle Mapper) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), are finally presented. Considering these results, sparse representation and Bayesian approaches seem far from being mature to be adopted in operational pan-sharpening scenarios

Fusion of VNIR Optical and C-Band Polarimetric SAR Satellite Data for Accurate Detection of Temporal Changes in Vegetated Areas

In this paper, we propose a processing chain jointly employing Sentinel-1 and Sentinel-2 data, aiming to monitor changes in the status of the vegetation cover by integrating the four 10 m visible and near-infrared (VNIR) bands with the three red-edge (RE) bands of Sentinel-2. The latter approximately span the gap between red and NIR bands (700 nm–800 nm), with bandwidths of 15/20 nm and 20 m pixel spacing. The RE bands are sharpened to 10 m, following the hypersharpening protocol, which holds, unlike pansharpening, when the sharpening band is not unique. The resulting 10 m fusion product may be integrated with polarimetric features calculated from the Interferometric Wide (IW) Ground Range Detected (GRD) product of Sentinel-1, available at 10 m pixel spacing, before the fused data are analyzed for change detection. A key point of the proposed scheme is that the fusion of optical and synthetic aperture radar (SAR) data is accomplished at level of change, through modulation of the optical change feature, namely the difference in normalized area over (reflectance) curve (NAOC), calculated from the sharpened RE bands, by the polarimetric SAR change feature, achieved as the temporal ratio of polarimetric features, where the latter is the pixel ratio between the co-polar and the cross-polar channels. Hyper-sharpening of Sentinel-2 RE bands, calculation of NAOC and modulation-based integration of Sentinel-1 polarimetric change features are applied to multitemporal datasets acquired before and after a fire event, over Mount Serra, in Italy. The optical change feature captures variations in the content of chlorophyll. The polarimetric SAR temporal change feature describes depolarization effects and changes in volumetric scattering of canopies. Their fusion shows an increased ability to highlight changes in vegetation status. In a performance comparison achieved by means of receiver operating characteristic (ROC) curves, the proposed change feature-based fusion approach surpasses a traditional area-based approach and the normalized burned ratio (NBR) index, which is widespread in the detection of burnt vegetation

Advantages of nonlinear intensity components for contrast-based multispectral pansharpening

In this study, we investigate whether a nonlinear intensity component can be beneficial for multispectral (MS) pansharpening based on component-substitution (CS). In classical CS methods, the intensity component is a linear combination of the spectral components and lies on a hyperplane in the vector space that contains the MS pixel values. Starting from the hyperspherical color space (HCS) fusion technique, we devise a novel method, in which the intensity component lies on a hyper-ellipsoidal surface instead of on a hyperspherical surface. The proposed method is insensitive to the format of the data, either floating-point spectral radiance values or fixed-point packed digital numbers (DNs), thanks to the use of a multivariate linear regression between the squares of the interpolated MS bands and the squared lowpass filtered Pan. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the intensity component no longer lie on a hypersphere in the vector space of the MS samples, but on a hyperellipsoid. Furthermore, before the fusion is accomplished, the interpolated MS bands are corrected for atmospheric haze, in order to build a multiplicative injection model with approximately de-hazed components. Experiments on GeoEye-1 and WorldView-3 images show consistent advantages over the baseline HCS and a performance slightly superior to those of some of the most advanced methodsPeer ReviewedPostprint (published version

Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo‐SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in‐situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L‐, C‐ and X‐bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non‐forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (°) was computed for each sensor‐polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L‐ and X‐ bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors

A support vector machine hydrometeor classification algorithm for dual-polarization radar

An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes in the flight route caused by unexpected adverse weather

PRISMA applications for territorial and urban planning

The Italian Tuscany Region, as part of its competences related to the knowledges of land use discipline, has been using, promoting, and maintaining functional territorial information since the 1970s. Nowadays, this activity is carried out not only for the needs of public administration technicians related to territory planning and management, but also for freelancers and citizens free information. However, while for some types of services in administrative procedures, territorial information from an aerial platform is still preferred, attention in recent years has also shifted to the possible use of optical satellite data. For this reason, the Tuscany Region in 2018 started an agreement with ASI and begun a pilot project in collaboration with UniSI and CNR. The project, still in progress, has as its first objective the mapping of the main materials that make up the city roofs (brick, cement conglomerates, bituminous conglomerates, metal, plastic, silicon, etc.) with the goal of implementing the attributes of their Land Use/Land Cover databases that are stably issued, every 3 years, since 2007. The first results clearly express the fundamental contribution of the hyperspectral data but, at the same time, highlight some intrinsic limits to the PRISMA mission, easily to be overcome and, we hope, interesting proposals for future hyperspectral missions

The FLUKA code for space applications: recent developments.

The FLUKA Monte Carlo transport code is widely used for fundamental research, radioprotection and dosimetry, hybrid nuclear energy system and cosmic ray calculations. The validity of its physical models has been benchmarked against a variety of experimental data over a wide range of energies, ranging from accelerator data to cosmic ray showers in the earth atmosphere. The code is presently undergoing several developments in order to better fit the needs of space applications. The generation of particle spectra according to up-to-date cosmic ray data as well as the effect of the solar and geomagnetic modulation have been implemented and already successfully applied to a variety of problems. The implementation of suitable models for heavy ion nuclear interactions has reached an operational stage. At medium/high energy FLUKA is using the DPMJET model. The major task of incorporating heavy ion interactions from a few GeV/n down to the threshold for inelastic collisions is also progressing and promising results have been obtained using a modified version of the RQMD-2.4 code. This interim solution is now fully operational, while waiting for the development of new models based on the FLUKA hadron-nucleus interaction code, a newly developed QMD code, and the implementation of the Boltzmann master equation theory for low energy ion interactions. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved