1,720,986 research outputs found
Improved Regression-Based Component-Substitution Pansharpening of Worldview-2/3 Data Through Automatic Realignment of Spectrometers
No full textThis 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
Harmonization of Multispectral and Panchromatic Images From VHR/EHR Spaceborne Scanners
No full textThis 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
Two-step fusion of operational land imager and thermal infrared spectrometer data based on nested hypersharpening and pansharpening stages
No full textWe propose a two-step spatial enhancement procedure for the two 100-m thermal infrared (TIR) bands of Landsat 8/9, captured by its TIR spectrometer (TIRS), approached as a problem of fusion of heterogeneous data or multimodal fusion. The fusion algorithm is guided by the statistical similarity between the TIR and visible/near-infrared (VNIR) and short-wave infrared (SWIR) bands provided at 30 m by the operational land imager (OLI). In the first step, hypersharpening is applied from the 100- to 30-m scale (3:10 scale ratio): the two TIRS bands are spatially enhanced by means of two linear combinations of the 30 m VNIR + SWIR bands, devised to maximize the correlation with each thermal band at its native 100-m scale. In the second step, the thermal bands, previously hypersharpened at 30 m, are pansharpened through the 15-m panchromatic (Pan) band of OLI. The proposed approach is compared with plain 100- to 15-m pansharpening carried out uniquely by means of the Pan image of OLI. Both visual evaluations and statistical indexes measuring radiometric and spatial consistency at the three scales are provided and discussed. The superiority of the two-step approach is highlighted
Full-Scale Regression Modeling of Spatial Details for Single-/Multiplatform Hypersharpening
No full textWhenever the sharpening band is not unique, the hypersharpening paradigm extends traditional pansharpening to any m-to-n fusion task, by integrating spatial information from multiple sources. The m-to-n fusion task is recast into multiple 1-to-n pansharpening problems by appropriately selecting or synthesizing a set of high-resolution (HR) bands to sharpen the set of low-resolution (LR) bands. The synthesis generates each of the sharpening bands as a linear combination of the available HR bands. The spectral coefficients of each synthetic band can be estimated using a multivariate linear regression (MLR) that matches the LR band to be sharpened. A different combination of the HR bands is assimilated to each LR band. Here, we propose a novel hypersharpening instance that directly combines high-pass spatial details, rather than lowpass image components. In general, fusion methods optimize their parameters at reduced scale, assuming a scale-invariance property. Instead, we introduce an estimation strategy that allows the fusion parameters to be directly retrieved at the full spatial scale. Starting from an iterative process, we derive an asymptotic closed-form solution and establish its convergence conditions. Three case studies involving as many real datasets—Sentinel-2, Environmental Mapping and Analysis Program (EnMAP), and WorldView-3—demonstrate performance improvements at reduced and full resolutions, obtained without any parametric optimization by the user, confirming the effectiveness and versatility of the proposed solution in single- and multiplatform fusion scenarios featuring diverse spatial resolutions, spectral bands, and resolution ratios
Spatial Resolution Enhancement of Prisma Hyperspectral Data Via Nested Hypersharpening with Sentinel-2 Multispectral Data
No full textThe 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
A proposal for full-scale processing and assessment of MS pansharpening
No full textIn this study, we propose to perform pansharpening fusion using multispectral (MS) data that have been harmonized with the encompassing panchromatic (PAN) image and to measure the quality using the harmonized data as a reference for consistency. Harmonization is a fast and robust procedure that removes mismatches, mainly aliasing artifacts, and shifts towards PAN. The results, presented on GeoEye-1 and WorldView-2 data with 10 pansharpening methods covering all possible approaches of the last twenty years, including neural ones, highlight: 1) that on harmonized data all methods work reasonably well in visual terms, while without harmonization, methods based on multiresolution analysis (MRA) produce less sharp results than those of component substitution (CS); 2) that the full-scale quality indexes, which penalized MRA methods with respect to CS in the absence of harmonization, are comparable and match visual quality. In this way, the fullscale tests are in accordance with the degraded-scale ones, where the mismatches between the datasets are reduced by the spatial downsampling. Unfortunately, degraded scale tests are no longer acceptable, since neural methods exist that can be trained on ground truth (GT), which is the original undegraded image, and is not available in practical cases. The intrinsic inconsistency of full-scale simulations had been blamed on the inadequacy of quality indexes. Instead, it depends on the mismatches of the data, which can be corrected. We believe that our study will open new horizons in the development of better and better pansharpening methods
Automatic Fine Co-Registration of Datasets from Extremely High Resolution Satellite Multispectral Scanners by Means of Injection of Residues of Multivariate Regression
Get PDFThis 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
Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data
Get PDFThe definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasicontinuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m : n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion
Spatial Resolution Enhancement of Satellite Hyperspectral Data via Nested Hypersharpening With Sentinel-2 Multispectral Data
Get PDFThis article presents an original method for the spatial resolution enhancement of satellite hyperspectral (HS) data by means of the Sentinel-2 visible and near infrared (VNIR) and short-wave infrared bands at 10 and 20 m spatial resolution. Presently, HS data are available from PRISMA (Italian acronym for HS precursor of the application mission) and Environmental Mapping and Analysis Program (EnMAP): both map the spectral interval of the solar radiation onto 240 and 224 bands, respectively, with 10 and 6.5/10 nm widths. A 5 m × 5 m panchromatic (PAN) band is also acquired by PRISMA. When the PAN band is unavailable, or better, the higher spatial resolution sharpening band is not unique, advantage can be taken from the hypersharpening protocol. First, 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 at 10 m as well, still according to the hypersharpening protocol. Eventually, the 10- m hypersharpened bands are pansharpened at 5 m by means of the PAN image, if available. Results show that for PRISMA the nested hypersharpening followed by pansharpening is better than plain HS pansharpening, both visually and according to full-scale indexes of spectral and spatial consistence. For EnMAP data, in which the PAN image is missing, the improvement of the fused data with respect to the original EnMAP and Sentinel-2 data has been quantified by means of two novel statistical indexes capable of measuring the spatial and intersensor consistencies between sharpened and sharpening data
Fast multispectral pansharpening based on a hyper-ellipsoidal color space
No full textIn this paper, we present a modified version of a popular component-substitution (CS) pansharpening method, namely the hyperspherical color space (HCS) fusion technique. Unlike other improvements of HCS, the proposed method is insensitive to the format of the data, either calibrated spectral radiance values or uncalibrated 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, in order to find out the intensity component peculiar of CS methods. The regression of squared MS, instead of the Euclidean radius used by HCS, makes the color space hyper-ellipsoidal instead of hyper-spherical and the intensity component more similar to the lowpass-filtered Pan, such that the extracted detail, namely Pan minus intensity, is more accurate. Furthermore, before the regression is calculated, the interpolated MS bands are diminished by their minima, in order to build a multiplicative injection model with approximately de-hazed components, thereby benefiting from the haze correction, as for all methods exploiting the multiplicative model. Experiments on true GeoEye-1 images show consistent advantages over the baseline HCS and its improvements achieved over time, and a performance comparable with some of the most advanced methods
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