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This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) 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 corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors. [ABSTRACT FROM AUTHOR]

Wood distillate (WD) has recently emerged as a promising bio-stimulant for sustainable legume crop production, owing to its ability to enhance seed yield and quality. However, no studies exist on the effects of WD on chickpea plants at pre-harvesting stages, hindering the farmers' ability to acquire valuable knowledge on the early action of WD on the plants' status and preventing the establishment of proactive measures to optimize WD use in agriculture. In this study, two multispectral, thermographic and spectroradiometric surveys, along with in-situ measurements of specific plant biometric traits, were conducted across the reproductive stage of field-grown chickpea in order to evaluate the early involvement of WD on plant health. The acquired multispectral images were used to calculate the Normalized Difference Vegetation Index (NDVI), revealing a notable ~35% increase in NDVI scores of WD-treated plants at the onset of physiological maturity, and indicating an improved plant status compared to the control (water-treated) plants. Moreover, control and WD-treated plants exhibited distinct spectral signatures across the visible, near-infrared (NIR) and short-wave infrared (SWIR) spectra, suggesting potential changes in their photosynthetic capacity, structural properties and water content both at the leaf and at the pod level. Furthermore, WD-treated plants showed a 25% increase in pod production, particularly at the beginning of seed maturity, suggesting that enhancements in plant status were also reflected in higher pod yields. These results point to a beneficial effect of WD on plant health during the preliminary stages of seed formation and indicate that a combination of both multispectral and spectroradiometric analyses can provide critical insights on the status of chickpea crops at pre-harvesting stages. In addition, these findings emphasize the importance of analyzing pre-harvesting stages to gain insights into the early involvement of WD in promoting plant health and, ultimately, in predicting final crop yields. [ABSTRACT FROM AUTHOR]

The detection and evaluation of biomass burning and dust events are critical for understanding their impact on air quality, climate, and human health, particularly in the Mediterranean region. This research pioneers an innovative methodology that uses Sentinel-2 multispectral (MS) imagery to meticulously pinpoint and analyze long-transport dust outbreaks and biomass burning phenomena, originating both locally and transported from remote areas. We developed the dust/biomass burning (DBB) composite normalized differential index, a tool that identifies clear, dusty, and biomass burning scenarios in the selected region. The DBB index jointly employs specific Sentinel-2 bands: B2-B3-B4 for visible light analysis, and B11 and B12 for short-wave infrared (SWIR), exploiting the specificity of each wavelength to assess the presence of different aerosols. A key feature of the DBB index is its normalization by the surface reflectance of the scene, which ensures independence from the underlying texture, such as streets and buildings, for urban areas. The differentiation involves the comparison of the top-of-atmosphere (TOA) reflectance values from aerosol events with those from clear-sky reference images, thereby constituting a sort of calibration. The index is tailored for urban settings, where Sentinel-2 imagery provides a decametric spatial resolution and revisit time of 5 days. The average values of DBB achieve a 96% match with the coarse-mode aerosol optical depths (AOD), measured by a local station of the AERONET network of sun-photometers. In future studies, the map of DBB could be integrated with that achieved from Sentinel-3 images, which offer similar spectral bands, albeit with much less fine spatial resolution, yet benefit from daily coverage. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimum algorithm, in tile 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 lull 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 Immse 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. Index Terms--Multispectral (MS) images, optimization, panchromatic (Pan) sharpening, quality assessment.

This paper 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-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. Index Terms--Intensity-hue-saturation (IHS) transformation, Landsat-7 Enhanced Thematic Mapper Plus (ETM+), multisensor data fusion, multispectral imagery, synthetic aperture radar (SAR) imagery, texture.

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. Index Terms--Coherence, information mining, intensity correlation, speckle, synthetic aperture radar (SAR) images, temporal correlation.

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. Index Terms--Context modeling, data fusion, Laplacian pyramid, multiresolution analysis, multispectral images, undecimated discrete wavelet transform (UDWT).

The GSM cellular system provides the European countries with a common digital mobile radio structure for overland communications. This paper illustrates an implementation of a GSM receiver in which two reduced complexity Viterbi processors are used: the first one for the demodulation of the GMSK signal and the second one for the decoding operations. The performance of the receiver is evaluated through a channel simulator for mobile communications. The results show that in many cases the proposed receiver allows the significant reduction of the computation complexity with respect to the classical Viterbi processor. In particular, for mean and high signal-to-noise ratios, a reduction of four times in the computational burden is obtained.