This study presents a new concept related to the quality and usability of multispectral (MS) and panchromatic (PAN) data sets captured by very-/extremely high-resolution (VHR/EHR) satellite MS scanners. Despite the ever-increasing resolution and spectral coverage capabilities of modern instruments, MS datasets suffer from the presence of aliasing originating from the insufficient sampling step size, corresponding to a high value of the modulation transfer function (MTF) of the instrument at the Nyquist frequency. Another source of degradation is the local misalignment between interpolated MS and PAN, originating from different viewpoints along the orbit of the instruments in the presence of discontinuities of the imaged surface. Here, we propose a procedure to harmonize MS and PAN datasets: aliasing artifacts of MS are suppressed; local MS-to-MS and MS-to-PAN shifts are damped. This is feasible for the unimodality of the MS and PAN instruments. Harmonization of MS towards PAN is achieved by injecting the least squares (LS) residue of the multivariate linear regression of the interpolated bands captured by each MS scanner towards the lowpass-filtered PAN. Experiments with two (MS-PAN) and three (MS1-MS2-PAN) on-board instruments show that an almost perfect alignment and a significant reduction of aliasing patterns are achieved. MS pansharpening greatly benefits from harmonized datasets. When the latter are used for the fusion and as references of spectral and spatial qualities of fusion products, inconsistencies noticed and complained about in the literature vanish. The procedure is in real time, fully automated, and does not require additional or ancillary information.

This work presents a pre-processing patch to automatically realign the multispectral (MS) spectrometers of WorldView-2 or WorldView-3. Once the resampled bands have been accurately overlaid onto the Pan image, established component substitution pansharpening algorithms can recover their original top performance, which had been diminished in the passage from a 4-band (a unique MS spectrometer) to an 8-band setup (two separate MS spectrometers). The misalignment arises because the three instruments onboard Worldview-2 (four on WorldView-3) – namely, old MS, new MS, and Pan – share the same optics and thus cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among the datasets. The three images can be accurately aligned only if the digital elevation surface model is exactly known. The proposed alignment procedure is fully automated and does not require any additional or ancillary information, but relies on the unimodality of the MS and Pan sensors.

The paper presents the activities of the TUS:CAN project, funded by ASI, the Italian Space Agency, and focuses on the experimental results obtained at the end of the first year. A Google Earth Engine implementation has been designed for spaceborne image collection and handling, spatial resolution enhancement, classification, and cross-validation with airborne hyperspectral image data and in situ spectral signature acquisitions. Initial trials using Sentinel-2 multispectral imagery over a 95 km2area validate the reliability and consistency of a technique to categorize and quantitatively assess natural and man-made land covers, aimed at updating maps and promoting sustainable land use.

The paper presents an original method for the spatial resolution enhancement of the hyperspectral (HS) data from PRISMA (Italian acronym for Hyperspectral Precursor of the Application Mission) by means of the Sentinel-2 visible and near infrared (VNIR) and shortwave infrared (SWIR) bands at 10 and 20 m spatial resolution, and the 5 m panchromatic (PAN) band also acquired by the PRISMA satellite. Firstly, the 20 m bands of Sentinel-2 are hypersharpened to 10 m by means of the four 10 m VNIR bands of the same instrument. Then, the 10 m hypersharpened bands of Sentinel-2 are used to sharpen the 30 m bands of PRISMA to 10 m as well, still according to the hypersharpening protocol. Eventually, the 10 m hypersharpened bands of PRISMA are pansharpened to 5 m by means of the Pan image of the same satellite. Results show that the nested hypersharpening followed by pansharpening is better than plain HS pansharpening, both visually and according to established full-scale indexes of spectral and spatial consistence.

The paper presents an unsupervised procedure for change mapping from two very-high-resolution (VHR) SAR acquisitions. Following the approach proposed in [1], the method exploits the characteristics of a robust, parametric, high-order statistics change feature by extending it to a multiscale version, namely Multi-scale Kullback-Leibler (MKL) feature. Its effectiveness is experimentally confirmed on simulated and real VHR amplitude SAR image pairs. The thresholding algorithm is revisited, optimized and tested on true CosmoSkyMed (CSK) images. The experimental tests demonstrate the robustness and quality of the proposed change mapping method, which can be applied for damage assessment in disaster management systems.

Building detection can be achieved manually by human experts from optical multispectral (MS) images. However, the time required to perform manual labeling and the landscape variability due to seasonal changes make this process very time consuming and almost unfeasible for large scale monitoring.

In this paper, we propose a totally unsupervised procedure to cope with the residual local misalignment between a higher-resolution panchromatic (Pan) image and a series of lower-resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. The proposed method exploits the different resolutions of the MS and Pan datasets to force the former to match a lowpass version of the latter. Specifically, the space-varying residue of the multivariate regression between resampled MS bands and lowpass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignment, is injected into each the MS bands, after being weighted by the pixel-varying multiplicative injection gain of each band. Tests on a GeoEye-1 image, with space-varying shifts, highlight improvements in the spatial alignment.

Several tasks of remote sensing entail the measurement of the similarity/dissimilarity of a test multiband image to a reference multiband image. To this purpose, several indices has been developed over the last two decades. The most widely used indices are normalized to avoid dependence on the data format. In this work, we will focus on such indices and provide a novel insight on their behaviors. Spectral indices are those performing crossed measurements between couple of different bands of the test and reference image. Wherever crossed measurements do not occur, the index is purely spatial, or better radiometric. Both theoretical insights and simulations performed on a GeoEye dataset, with the products of twelve pansharpening methods, show that their performance ranking does not depend on the data format for purely radiometric indices, while it significantly depends on the data format, either spectral radiance or digital numbers (DN), for a purely spectral index, like the spectral angle mapper (SAM). The dependence on the data format is weak for indices that balance the spectral and radiometric similarity, like the family of indices, Q2n, based on hypercomplex algebra.

The paper presents an efficient unsupervised change mapping algorithm for 1-look amplitude SAR images. The method exploits the complementary characteristics of two change features and proposes a morphological non-parametric map combination to produce the final change map. The method is able to preserve the geometry of the changed regions without increasing the overall false alarm rate PFA. This is made possible by morphologically combining the binary change maps obtained from a low-PFAhigh-order statistics (CKLD) change feature and a detail-preserving multiscale ratio detector. A new thresholding method is also proposed for the CKLD feature. Experimental tests, performed on simulated 1-look SAR images, visually and objectively demonstrate the advantages of the proposed algorithm.

Pansharpening usually refers to the fusion of a high spatial resolution panchromatic image with a low spatial resolution multispectral image. One of the most debated issue in this research field regards the quality assessment of fused products. The two exploited quality assessments are at reduced resolution and at full resolution. The former is an accurate procedure, but the main drawback is that it works on synthetic (with lower spatial resolutions) products. The latter is able to work at full resolution paying it with a reduced accuracy due to the absence of a ground-truth. In this work, we will focus on the assessment at full resolution by introducing a new measure of spatial consistency based on multivariate linear regression of the panchromatic image towards the multispectral bands. Simulations with an IKONOS dataset and six fusion methods show that the proposed spatial index is the ideal counterpart of Khan's spectral consistency index.

Very-High-Resolution Synthetic Aperture Radar (VHR-SAR) images are of particular interest to characterize and monitor urban areas at a global scale. While automatic urban footprint extraction from SAR images can be theoretically performed at very high spatial and temporal resolutions, in practice it requires a huge amount of processing time and memory resources for a global coverage. The paper presents a fast procedure for 1m-resolution spotlight mode scene analysis which shows limited memory requirements and robustness to one-look speckle disturbance. The proposed method adopts a multiscale approach, with a radiometrically scalable scheme, and exploits theoretic electromagnetic scattering models of buildings. Experimental results with objective assessment on spotlight Cosmo-SkyMED images will be presented and validated on reference urban classes provided by the Copernicus Urban Atlas 2012.

In this letter, we propose a simple yet robust procedure to combat the residual local misalignment between the image data sets that are usually processed for pansharpening: a higher resolution panchromatic (Pan) image and a series of lower resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. Unlike a conventional coregistration, which requires a preliminary orthorectification enforced by an accurate digital surface model, the proposed method automatically exploits the characteristics of the Pan image to alleviate the effects of misalignment on fusion products, whichever is the method chosen for pansharpening. More specifically, the space-varying residue of the multivariate regression between resampled MS bands and low-pass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignments, is injected into the MS bands after being weighted by the projection coefficients of each band. Tests on simulated Pléiades images demonstrate that global shifts up to five pixels along each direction are perfectly compensated. Tests on a true GeoEye-1 image, whose shifts are space-varying and of unknown extent, highlight the attained improvement in spatial alignment.

This paper addresses the problem of exploiting very high-resolution multifrequency SAR data collected by the COSMO-SkyMed and RADARSAT-2 missions to support risk monitoring and assessment in urban and suburban areas. The proposed approach aims at taking benefit from the synergy between the two SAR data sources to optimize the accuracy of thematic products of interest to risk monitoring. In particular, an unsupervised change detection approach is discussed, in which feature-level fusion is applied to a satellite image time series including both COSMO-SkyMed and RADARSAT-2 acquisitions to improve the detection results as compared to those generated through single-frequency processing.

In this letter, we show that pansharpening of visible/near-infrared (VNIR) bands takes advantage from a correction of the path-radiance term introduced by the atmosphere during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth's surface from space, that is, for methods exploiting a contrast-based injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. Such methods are high-pass modulation (HPM), Brovey transform, synthetic variable ratio (SVR), University of New Brunswick pansharp, smoothing filter-based intensity modulation, and spectral distortion minimization. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished and added back after. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized SVR and HPM algorithms. Simulations carried out on QuickBird and IKONOS data highlight that haze correction of MS before fusion is always beneficial, especially on vegetated areas and in terms of spectral quality.

In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred.

In this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detail-injection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature.

Urinary Tract Infections (UTIs) are very common in women, babies and the elderly. The most frequent cause is a bacterium, called Escherichia coli, which usually lives in the digestive system and in the bowel. Infections can target the urethra, bladder or kidneys. Traditional analysis methods, based on human experts' evaluation, are typically used to diagnose UTIs, an error prone and lengthy process, whereas an early treatment of common pathologies is fundamental to prevent the infection spreading to kidneys. This paper presents an image based Automated Bacterial Load Estimator (ABLE) system for the urinoculture screening, that provides quick and traceable results for UTIs. Infections are accurately detected and the bacterial load is evaluated through image processing techniques. First, digital color images of the Petri dishes are automatically captured, and cleaned from noisily elements due to laboratory procedures, then specific spatial clustering algorithms are applied to isolate the colonies from the culture ground and, finally, an accurate evaluation of the infection severity is performed. A dataset of 499 urine samples has been used during the experiments and the obtained results are fully discussed. The ABLE system speeds up the analysis, grants repeatable results, contributes to the process standardization, and guarantees a significant cost reduction.

The majority of multispectral (MS) pansharpening methods may be labeled as spectral or spatial, depending on whether the geometric details that shall be injected into the interpolated MS bands are extracted from the panchromatic (P) image by means of a spectral transformation of MS pixels or a spatial transformation of the P image, achieved by means of linear shift-invariant digital filters. Spectral methods are known as component substitution; spatial methods are based on multiresolution analysis (MRA). In this paper, the authors show that, under the most general conditions, MRA-based pansharpening is characterized by a unique separable low-pass filter, which can be parametrically optimized based on the modulation transfer function (MTF) of the MS instrument, possibly followed by decimation and interpolation stages. This happens for the discrete wavelet transform (DWT) and its undecimated version (UDWT), for the “à-trous” wavelet (ATW) transform and its decimated version, i.e., the generalized Laplacian pyramid (GLP), and for nonseparable wavelet transforms, such as the nonsubsampled contourlet transform (NSCT). Hybrid methods, in which MRA fusion is performed on the intensity component derived from a spectral transformation, are equivalent to MRA fusion with a specific detail injection model. ATW and GLP are preferable to DWT, UDWT, and NSCT, because of computational benefits and of a looser choice of the low-pass filter, unconstrained from the requirement of generating a perfect reconstruction filter bank. Ultimately, GLP outperforms ATW, because its decimation and interpolation stages allow the aliasing impairments intrinsically present in the original MS bands to be removed from the pansharpened product.

Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.

The paper presents a new algorithm for the estimation of the heights of buildings acquired by multiangular multispec-tral/panchromatic acquisitions. The proposed method aims at exploiting the complete multiangular spatial and spectral information provided by the WorldView-2 system with controlled computational complexity of the overall automated system. It adopts a hierarchical classification scheme to detect the tops of the buildings from multangular pansharpened multispectral images of the considered urban scenario. Given five angular acquisitions, the (up to) four displacements of a rooftop with respect to the most nadiral view provide an accurate estimate of the height of the building. The experimental results show that the proposed method provides reliable and accurate building height estimates when compared to the true heights provided by publicly available building databases.

Polarimetric techniques applied to radars on-board civil aircraft can improve the estimation of risk zones due to dangerous weather during flight. Usually, X-band weather radars are installed at the nose of civil aircrafts. This band is affected by strong attenuation in case of intense precipitation (liquid or mixed phase). The current systems do not compensate backscattered power measurements for attenuation caused by propagation through precipitation, while dual-polarization radars are able to compensate effectively this source of error and to discriminate hydrometeors. In this work, a classification algorithm based on support vector machines (SVM) is proposed. Training is driven by the output of a Fuzzy Logic (FL) classification algorithm (typical classification approach used for ground-based weather radar). SVM high performance in terms of time processing and its flexibility of configuration using all type of inputs variables are important characteristics to be included in some avionic specific equipment, such as the Electronic Flight Bag (EFB). Two datasets have been used to test the SVM classification algorithm. The first dataset is composed of simulated radar polarimetric observations at X-band and the second one is composed of actual dual-polarization radar measurements collected during the Special Observation Period (SOP) 1.1 of HYdrological cycle in MEditerranean EXperiment (Hymex) campaign by the C-band Doppler dual-polarization weather radar (Polar 55C) installed at ISAC-CNR in Rome. Good performance are obtained for SVM classificator. The comparison with FL output shows a good agreement (up to 90%) both in qualitative comparison maps by maps and using a quantitative approach which metric is based on the confusion matrix.

High-quality pansharpened multispectral (MS) images are rarely obtained from fast, efficient, and robust algorithms. In most cases, effective pansharpening methods have huge computational complexity, as in the case of variational methods, or algorithms based on sparse representations. Moreover, injection models are often application dependent, not sufficiently general to be applied to different scenarios, and the resulting algorithm implementations cannot process large-size images. The proposed pansharpening method is accurate and fast and can be successfully applied to huge images. It also solves the problem of contextadaptive schemes that tune the spatial injection parameters on local statistics: Instabilities and blocky artifacts can be generated by pansharpening methods whose parameters are computed on local windows. The proposed method is an extension of the classical component-substitution algorithms: An optimal detail image (in the mmse sense) extracted from the panchromatic band is calculated for each MS band by evaluating band-dependent generalized intensities. It overcomes window-based local estimation of parameters by applying a nonlocal parameter optimization through K-means clustering. Very high quality scores, both at degraded and full scale, and excellent visual quality of the fused images demonstrate the validity of the method.

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.

The paper presents a pansharpening algorithm that finds an optimal linear solution, in the MMSE sense, following a generalized component-substitution approach. It is characterized by nonlocal parameter optimization obtained through K-means clustering. The proposed method, namely C-BDSD, solves the problem of context-adaptive schemes that tune the spatial injection parameters on local statistics: instabilities and blockiness artifacts are avoided and the estimation phase is improved. The C-BDSD algorithm is accurate and fast, and can be also applied to spatially enhance large-size multi-spectral images. Very high quality scores and excellent visual quality of the fused images demonstrate the validity of the method.

In this paper state-of-the-art and advanced methods for multispectral pansharpening are reviewed and evaluated on two very high resolution datasets acquired by IKONOS-2 (four bands) and WorldView-2 (eight bands). The experimental analysis allows us to highlight the performances of the two main pansharpening approaches (i.e. component substitution and multiresolution analysis).

In this work we present and discuss the effects of different scintillation models on the Normalize Differential Spectral Attenuation (NDSA) measurements in a counter-rotating Low Earth Orbiting (LEO) set event at 17, 19, 21, 32, 179 and 182 GHz. Some parameters of the scintillation models used here are computed at global scale using an European Centre for Medium-Range Weather Forecasts (ECMWF) global dataset. The scintillation effects on the NDSA measurements are analyzed in terms of relative error of the their estimation.

This paper presents a modified version of the information-theoretic feature in [1] for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method is capable of capturing small-area structural changes between the two images, by maintaining at the same time the capability of rejecting statistical changes due to speckle patterns or co-registration inaccuracies. This improvement is obtained by bootstrapping the original procedure by means of an adaptive preliminary selection of potentially changed pixels driven by the logarithm of the pixel ratio. Experimental results have been carried out on true SAR images acquired by the COSMO-SkyMed constellation.

A nonparametric method for unsupervised change detection in multipass synthetic aperture radar (SAR) imagery is described. The method relies on a novel feature capturing the structural change between two SAR images and is robust to the statistical change that may be originated by speckle and coregistration inaccuracies. The proposed method starts from the scatterplot of the amplitude levels in the two images and applies the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. If we assume that the two images have been preliminarily coregistered and calibrated on one another, then all the modes lying outside the main diagonal correspond to the structural changes across the two observations. The value of the probability density function (PDF) in any of the off-diagonal modes found by the MS algorithm is translated into a value of conditional information. This value is assigned to all image pixels generating the corresponding cluster in the scatterplot. Thus, a feature is obtained on a per-pixel basis. Experimental results on simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation show that the proposed feature exhibits significantly better discrimination capability than the classical log-ratio (LR). Advantages over a preliminary version of the method without MS regularization and over another nonparametric method based on Kullback-Leibler divergence are also demonstrated. The method is robust when it is applied to SAR images with different acquisition angles, whose effects are deemphasized compared to the actual scene changes.

This paper presents a fast method suitable for pansharpening of MS imagery. Key points of the novel method, which falls in the category of component substitution (CS) methods, are optimization of the intensity component, achieved through multivariate regression of Pan to MS, and adjustment of the modulus of the spatial detail vector to be injected, based on a minimization of spatial distortion. Spatial distortion is measured at full scale according to the QNR protocol on land cover classes defined by NDVI thresholding. Experiments carried out on IKONOS data demonstrate that results are competitive with those of the most advanced methods, with a computational complexity comparable with that of Brovey transform fusion, which is the baseline version of the proposed method.

This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles.

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.

Sea Normalized RCS, and Doppler spectra have been revised for HF-OTH Clutter Modelling. The Hasselmann model is firstly introduced to predict the sea directional spectrum of fetch-limited sea and results have been compared with the Pierson-Moskovitz model used for large scale ocean remote sensing. Results show that the closed fetch-limited sea has lower NRCS compared with ocean for similar wind intensity and direction. For this reasons RCS and Doppler spectra must be predicted taking into account of the fetch dimension. In future work we will generalize this interesting approach to fetch-limited wind, time-limited pulse, in order to show the waveform effect on Doppler spectrum.

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.

We present an experimental study on the effects of pansharpening on target detection from multispectral and panchromatic images acquired by very-high resolution satellite sensors. Original and spatially-enhanced image data are used to detect relatively small targets (vehicles on an airport scenario) characterized by a sufficiently well-defined spectral signature in the four MS bands. The aim of the paper is to compare different pansharpening methods by evaluating the performances of target detection on true MS and panchromatic data. Both linear and kernel-based one-class classification methods are considered for the target detection process.

A semi-supervised graph-based approach to target detection is presented. The proposed method improves the Kernel Orthogonal Subspace Projection (KOSP) by deforming the kernel through the approximation of the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a hyperspectral image target detection application for thermal hot spot detection. An improvement is observed with respect to the linear and the non-linear kernel-based OSP, demonstrating good generalization capabilities when low number of labeled samples are available, which is usually the case in target detection problems.

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.

The orthogonal subspace projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel methods (KMs) makes the method nonlinear, helps to combat the high-dimensionality problem, and improves robustness to noise. This paper presents a semisupervised graph-based approach to improve KOSP. The proposed algorithm deforms the kernel by approximating the marginal distribution using the unlabeled samples. Two further improvements are presented. First, a contextual selection of unlabeled samples is proposed. This strategy helps in better modeling the data manifold, and thus, improved sensitivity-specificity rates are obtained. Second, given the high computational burden involved, we present two alternative formulations based on the Nystroumlm method and the incomplete Cholesky factorization to achieve operational processing times. The good performance of the proposed method is illustrated in a toy data set and two relevant hyperspectral image target-detection applications: crop identification and thermal hot-spot detection. A clear improvement is observed with respect to the linear and the nonlinear kernel-based OSP, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in target-detection problems. The relevance of unlabeled samples and the computational cost are also analyzed in detail.

The Orthogonal Subspace Projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel helps to combat the high dimensionality problem and makes the method robust to noise. This paper presents a semi-supervised graph-based approach to improve KOSP. The proposed algorithm deforms the kernel by approximating the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a toy dataset and an hyperspectral image target detection problem.

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 a Weighted Least Squares estimator to calculate injection parameters in the fusion model. For each pixel of the image a weight is calculated by a classification map. The classifier used in the experiments is a Support Vector Machine in order to obtain high accuracy on each land-cover type. Results are presented and discussed on very-high resolution images acquired by Quickbird and Ikonos satellite systems. Fusion simulations on spatially degraded data and fusion tests at full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.

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.

Multi-temporal analysis of Synthetic Aperture Radar (SAR) images has gained an ever increasing attention due to the availability of several satellite platforms with different revisit times and to the intrinsic capability of the SAR system of producing all-weather observations. As a drawback, automated analysis in general and change detection in particular are made difficult by the inherent noisiness of SAR imagery. Even if a pre-processing step aimed at speckle reduction is adopted, most of algorithms borrowed from computer vision cannot be profitably used. In this work, a novel pixel feature suitable for change analysis is derived from information-theoretic concepts. It does not require preliminary de-speckling and capable of providing accurate change maps from a couple of SAR images. The rationale is that the negative of logarithm of the probability of an amplitude level in one image conditional to the level of the same pixel in the other image conveys an information on the amount of change occurred between the two passes. Experimental results carried out on two couples of multi-temporal SAR images demonstrate that the proposed IT feature outperform the Log-Ratio in terms of capability of discriminating changes.

This paper presents two novel image fusion methods, suitable for sharpening of hyperspectral (HS) images by means of a panchromatic (Pan) observation: the HS bands expanded to the finer scale of the Pan image are sharpened by adding the spatial details which are calculated by the PAN image. Since a direct, unconditioned injection of Pan details gives unsatisfactory results, a new injection model is proposed, which provides the optimum injection simulating fusion at degraded scale by minimizing the mean square error. Fusion tests are carried out both on spatially degraded data to objectively compare the proposed scheme to some fusion methods and on full resolution image data.

This work focuses on quality assessment of fusion of hyperspectral (HS) images with high-resolution panchromatic (Pan) data. A novel fidelity index suitable for HS images is defined from the theory of hypercomplex numbers (2n-ons). Both spectral and spatial distortion measurements are encapsulated in a unique score index. Some fusion methods capable to selectivity inject spatial-frequencies from the higher-resolution Pan image to the coarser HS bands are used for testing and comparisons. Experimental results are presented on Hyperion and ALI data sets.

In this work we investigate the use of Shannon's information theory for the goal of devising quality scores of image fusion results that do not require reference originals. In particular, the mutual information between resampled original and fused MS bands is used to measure the spectral quality, while the mutual information between the Pan image and the fused bands yields a measure of spatial quality. The rationale is that the normalized mutual information calculated either between any couple of bands, or between each MS band and the Pan image, should be unchanged after fusion, i.e., when passing from the coarse scale of the MS data to the fine scale of the Pan image. Experimental results carried out on QuickBird and Ikonos data demonstrate that the results provided by the proposed information-theoretic method are in trend with analysis performed on spatially degraded data by means of such parameters as Walds's ERGAS, Wang and Bovik's QI, and the novel Q4 score index based on quaternion theory and recently proposed by the authors. However, the novel method requires no reference and is therefore directly applicable in all practical cases

This paper presents a novel image fusion method, suitable for pan-sharpening of multispectral (MS) bands, based on multiresolution analysis (MRA). The low-resolution MS bands are sharpened by injecting highpass directional details extracted from the high-resolution panchromatic (Pan) image by means of the curvelet transform, which is a nonseparable MRA, whose basis function are directional edges with progressively increasing resolution. The advantage with respect to conventional separable MRA, either decimated or not, is twofold: directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve denoising better than in the separable wavelet domain; modeling of the relationships between high-resolution detail coefficients of MS bands and of the Pan image is more fitting, being carried out in a directional wavelet domain. Experiments carried out on a very-high resolution MS + Pan QuickBird image show that the proposed curvelet method quantitatively outperforms state-of-the art image fusion methods, in terms of geometric, radiometric, and spectral fidelity

SAR images from space-borne platforms have proved to be helpful data for identification of oil spills and other surface anomalies, such as low wind areas, man-made targets, and natural films. The use of fractal dimension, which is related to the concept of surface "roughness", as a feature for classification, improves the detection of anomalies, since enhances texture discrimination. In the particular case of oil slicks, the surface tension of seawater is increased and the surface wave motion is significantly depressed. This effect relatively reduces the sea surface roughness, decreases the radar backscattered energy and enables oil slicks to be discernible from the radar image. Several algorithms may be applied for local fractal dimension estimation, but most solutions are tailored for specific applications and are characterized by estimation accuracies depending on the adopted image model and also on the value being estimated. This paper describes a decision-based fusion approach for local fractal dimension estimation of SAR images of the sea surface. Three different estimation algorithms are considered and the three resulting fractal maps are fused by means of a weighted average. The weights are calculated from the performance characteristics of the three algorithms measured on synthetic fractal surfaces. The experimental results carried out on ERS-2 SAR images prove the effectiveness of the proposed decision-based fusion approach

This paper critically reviews state of the art and advanced methods for multispectral (MS) and panchromatic (Pan) image fusion based on either intensity-hue-saturation (IHS) transformation, or redundant multiresolution analysis (MRA). In either case, lower-resolution MS bands are sharpened by injecting details taken from the higher-resolution Pan image. Crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extended to the finer resolution to weight the Pan details to be injected. Two injection models embedded in an "a trous" wavelet decomposition will be compared on a test set of very high resolution QuickBird MS+Pan data. One works on approximations and provides a partial unmixing of coarse MS pixels via high-resolution Pan. Another is based on spectral fidelity of original and merged MS data. Fusion comparisons on spatially degraded data, whose high-resolution MS originals are available for reference, show that the former performs better than the latter, in terms of both spatial and spectral fidelity

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.

This work presents a novel multisensor image fusion algorithm, which extends panchrmomatic sharpening of multispectral (MS) data through intensity modulation to the integration of MS and synthetic aperture radar (SAR) imagery. The method relies on SAR texture, extracted by ratioing the despeckled SAR image to its low-pass approximation. SAR texture is used to modulate the generalized intensity (GI) of the MS image, which is given by a linear transform extending intensity-hue-saturation transform to an arbitrary number of bands. Before modulation, the GI is enhanced by injection of high-pass details extracted from the available panchrmomatic image by means of the "a/spl grave/-trous" wavelet decomposition. The texture-modulated panchrmomatic-sharpened GI replaces the GI calculated from the resampled original MS data. Then, the inverse transform is applied to obtain the fusion product. Experimental results are presented on Landsat-7 Enhanced Thematic Mapper Plus and European Remote Sensing 2 satellite images of an urban area. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes.

This work presents an unsupervised method capable to provide estimates of temporal coherence starting from a couple of multilook detected SAR images of the same scene. The method relies on a robust measurement of the temporal correlation of speckle patterns occurring between the two pass dates. Thanks to the accurate speckle assessment, the temporal correlation coefficient (TCC) of speckle between two overlapped images taken different times is estimated. A nonlinear transformation aimed at decorrelating the data across time while retaining the multiplicative noise model is defined starting from the pixel the pixel geometric mean and ratio of the two overlapped observations. Such a reversible transformation is applied to the couple of images to expedite assessment of temporal speckle patterns correlation. The TCC of speckles is estimated from the noise variances of a transformed couple of images by inverting the relationship yielding the noise variances of the transformed data. Experiments are carried out on two SAR observations from the ERS-1/2 Tandem mission. Starting form the SLC pair, coherence is first estimated to be used as reference. Then, detected 5-looks images are produced and TCC is measured on square blocks, to yield the desired coherence estimate. A linear regression fit shows a good degree of matching with the true coherence values, which holds also on textured areas. Experiments show a good degree of accuracy, when the TCC of speckles is estimated on 32 /spl times/ 32 blocks of geometric mean and ratio of detected 5-looks amplitude images. The method yields acceptable results also in the presence of strong reflectors and textures (urban area) where intensity-based coherence estimators generally fail.

This work presents a viable solution to the problem of merging a multispectral image with an arbitrary number of bands with a higher resolution panchromatic observation. The proposed method relies on the generalized Laplacian pyramid, which is a multiscale oversampled structure in which spatial details are mapped on different scales. The goal is to selectively perform spatial-frequencies spectrum substitution from an image to another with the constraint of thoroughly retaining the spectral information of the coarser data. To this end, a vector injection model has been defined: at each pixel, the detail vector to be added is always parallel to the approximation. Furthermore, its components are scaled by factors measuring the ratio of local gains between the multispectral and panchromatic data. Such a model is calculated at a coarser resolution where both types of data are available and extended to the finer resolution by embedding the modulation transfer function of the multispectral scanner into the multiresolution analysis. In this way, the interband structure model can be extended to the higher resolution without the drawback of the poor enhancement occurring when the model assumes MTFs close to be ideal. Results are presented and discussed on very high resolution QuickBird data of an urban area.

The aim of this paper is to investigate whether fractal maps extracted from sea SAR images are useful for discriminating the sea from other entities or anomalies. Fractal mapping consists of locally estimating the fractal dimension of the image. To this purpose four different methods based on covering and spectral analysis are proposed and compared when applied to real ERS1-2 GEC images. Wind falls, sea and line coast are well distinguishable in the fractal maps. This result clearly shows that the use of image fractal processing is a promising and powerful technique for identifying sea surface anomalies.

This paper presents an unsupervised method capable to provide estimates of temporal coherence starting from a pair of multilook detected synthetic aperture radar (SAR) images of the same scene. The method relies on robust measurements of the temporal correlation of speckle patterns between the two pass dates. To this end, a nonlinear transformation aimed at decorrelating the data across time while retaining the multiplicative noise model is defined as the pixel geometric mean and ratio of the two overlapped images. The temporal correlation coefficient (TCC) of speckle is analytically derived from the noise variances, measured in the transformed pair of images as regression coefficients of local standard deviation to local mean, calculated on homogeneous, i.e., nontextured, pixels. Such pixels are identified based on the observation that homogeneous areas produce clustered scatter-points that are aligned along the regression line. Experiments were carried out on two pairs of multitemporal SAR observations, from the European Remote Sensing 1/2 (ERS-1/2) tandem mission and from the 1994 SIR-C mission. A good fit with the true coherence values was found, irrespective of the presence of textures; when the true coherence was unavailable, the estimated coherence results match the available ground truth data.

Fractal dimension of the sea surface is strictly related to its roughness, and may thus be helpful in determining the sea state, or where motion-damping oil spills are located. A possible way to determine the fractal dimension of the sea surface is that of performing a fractal analysis of remote sensing images, in particular satellite images, which have the advantage of observing a large area at one time. This paper aims to show the utility of fractal analysis of ERS-1 SAR images, and presents the results obtained by three different algorithms. The considered data was sensed by ERS-1 in the Mediterranean Sea at times and locations suitable for comparison with data coming from the Italian "Sistema Ondametrico Nazionale," an environmental measurement system including a number of buoys carrying accelerometers and communications instruments. The experimental results show some accordance between the buoy data and the ERS-1 fractal analysis outcome, but more data are required to provide statistical support to the conclusions.

This paper proposes a spectral analysis algorithm for sea SAR image fractal dimension estimation. The power spectral density of the sea SAR image is obtained through the periodogram approach and by decomposing the spectrum into sub-bands according to a dyadic scale. The fractal dimension of the image is obtained by a linear regression of the absolute maxima of each sub-band. This method can be applied globally or locally to have a single value or a map of the image fractal dimension. Examples on simulated and real data are reported.

The paper reviews some of the most efficient wavelet approaches to image fusion for remote sensing applications and indicates open issues and future trends of this remarkable research. For the particular topic of spatial enhancement in multi-resolution image fusion, quality assessment of the fused image is considered. An objective analysis is described, based on both global and local score indexes. The problem of reducing possible visual artifacts is also discussed.

This paper compares two general and formal solutions to the problem of fusion of multispectral images with high-resolution panchromatic observations. The former exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and upsampling the wavelet filter bank. The latter relies on the generalized Laplacian pyramid, which is another oversampled structure obtained by recursively subtracting from an image an expanded decimated lowpass version. Both the methods selectively perform spatial-frequencies spectrum substitution from an image to another. In both schemes, context dependency is exploited by thresholding the local correlation coefficient between the images to be merged, to avoid injection of spatial details that are not likely to occur in the target image. Unlike other multiscale fusion schemes, both the present decompositions are not critically subsampled, thus avoiding possible impairments in the fused images, due to missing cancellation of aliasing terms. Results are presented and discussed on SPOT data.

The idea of the present scheme is to apply a directional version of geometric filter (complementary-hull) to the different sub-bands into which the noisy image is decomposed by the S-transform, a dyadic Haar wavelet yielding integer valued coefficients. The hull algorithm is applied only on the direction(s) along which the signal is more structured. The number of iterations is adjusted to the SNR of the sub-bands, so as to preserve spatial details to the largest extent. Comparisons with the standard geometric filter are presented for images affected by synthetic multiplicative (speckle) noise. Results are pretty superior to those achieved without multiresolution context, both visually and in terms of SNR.

A FEC coding strategy compatible with the H.263 standard is proposed, which uses unequal error protection to improve image quality for low bit rate video transmission over noisy channels. The H.263 bit stream is partitioned into classes of different sensitivity, without introducing any modification into the standard stream syntax. The experimental results show that the proposed scheme can be used either when video is transmitted over channels having high round-trip delay and limited bandwidth, or as a FEC segment of an integrated FEC-ARQ strategy.

Two edge-preserving segmentation algorithms for multiband images are proposed in this paper. In particular, when dealing with multipolarization SAR data, adaptive neighborhood structures are selected for modelling polarimetric complex amplitudes and region labels, and for achieving detail-preservation. Experimental results show that the novel schemes produce significant visual improvements for detail preservation, and exhibit equivalent or higher classification performance with respect to the classical classification schemes. These results have been obtained from multiband, polarimetric SAR SIR-C data, selected for archaeological application studies.