Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions

Alcaras, Emanuele; Parente, Claudio; Vallario, Andrea

doi:10.3390/rs13081550

Cited by 19 publications

(15 citation statements)

References 68 publications

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“…The experiment results confirm that the choice of the best method is a challenging and nonunivoque task, and it depends on the needs of the user. For consequence, as reported in Alcaras et al, (2021), a multi-criteria analysis can be carried out to choose the most suitable method depending on the situation.…”

Section: Resultsmentioning

confidence: 99%

“…The first group remarks the spectral difference introduced by the pan-sharpening between the fused image and the initial MS image (Shahdoosti & Ghassemian, 2014). The second group remarks the conservation level of the spatial details assured by the pan-sharpening in terms of similarity between the object contours in the fused image and the corresponding one in the panchromatic image (Alcaras et al, 2021). Pansharpening techniques can be applied in many fields such as cultural heritage preservation (Baiocchi et al, 2017), shadow detection (Meneghini & Parente, 2015), vegetation mapping (Ibarrola-Ulzurrun et al, 2017), urban development (Hu et al, 2015), coastline evolution (Maglione et al, 2015), etc.…”

Section: *Corresponding Authormentioning

confidence: 99%

“…Spectral analysis, based on appositive indices, is required to establish the spectral similarity between MS and the corresponding MS'. Spatial analysis, also based on appositive indices, is useful to derive the similarities between the shape of the objects included in the MS' and the corresponding one in the PAN (Alcaras et al, 2021).…”

Section: Pan-sharpening Evaluationmentioning

confidence: 99%

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Comparison of Different Pan-Sharpening Methods Applied to Ikonos Imagery

Alcaras¹,

Corte²,

Ferraioli³

et al. 2021

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On board the IKONOS satellite there are sensors operating in the panchromatic and multispectral range: the geometric resolution of the acquired images is higher in the first case (1 m) than in the second one (4 m); on the contrary, panchromatic images have lower spectral resolution than the latter. Pan-sharpening methods allow to reduce the pixel dimensions of the multispectral images to comply with the panchromatic resolution. In this way, it is possible to obtain enhanced detailed data in both geometric and spectral resolution. This work aims to compare the results obtained from the application of eight different pan-sharpening methods, which are totally carried out by using the raster calculator in QGIS: Multiplicative, Simple Mean, Brovey Transformation, Brovey Transformation Fast, Intensity Hue Saturation (IHS), IHS Fast, Gram-Schmidt, and Gram-Schmidt Fast. Each resulting dataset is compared with the original one to evaluate the performance of each method by the following quality indices: Correlation Coefficient (CC), Universal Image Quality Index (UIQI), Relative Average Spectral Error (RASE), Erreur Relative Global Adimensionnelle de Synthèse (ERGAS), Spatial Correlation Coefficient (SCC) and Spatial ERGAS (SERGAS); however, this is a difficult task because the quality of the fused image depends on the considered datasets. Finally, a comparison the various between methods is carried out.

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Section: Resultsmentioning

confidence: 99%

Section: *Corresponding Authormentioning

confidence: 99%

Section: Pan-sharpening Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Different Pan-Sharpening Methods Applied to Ikonos Imagery

Alcaras¹,

Corte²,

Ferraioli³

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first group remarks the spectral difference introduced by the pan-sharpening between the fused image and the initial MS image (Shahdoosti & Ghassemian, 2014). The second group remarks the conservation level of the spatial details assured by the pan-sharpening in terms of similarity between the object contours in the fused image and the corresponding one in the panchromatic image (Alcaras et al, 2021). Pansharpening techniques can be applied in many fields such as cultural heritage preservation , shadow detection (Meneghini & Parente, 2015), vegetation mapping (Ibarrola-Ulzurrun et al, 2017), urban development (Hu et al, 2015), coastline evolution (Maglione et al, 2015), etc.…”

Section: Comparison Of the Modelsmentioning

confidence: 99%

Geomatics Application for Geography

2021

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The papers in this special issue cover various areas related to Geomatics Applications for Geography as cartography, GIS, remote sensing, photogrammetry and GNSS, which are typical tools used by geoinformation researchers.A very interesting first case study has been developed by Pepe et al. in wich was identified an appropriate survey technique and numerical method for the calculation of volumes extracted in a quarry; also, the impact of the TIN (Triangulated Irregular Network) and GRID method was evaluated firstly, by the use of UAV (Unmanned Aerial Vehicle) photogrammetry and in latter the ease and speed in performing the 3D model by UAV photogrammetry was shown in the manuscript (Pepe et al. 2021).GNSS application was used in two papers: in the work of Innac et al. was evaluate the performance assessment of a Single Point Positioning algorithm (SPP), with a Kalman filter (KF) estimator, adapted for maritime applications, in wich the algorithm is developed in Matlab environment and tested using multi-GNSS single-frequency raw data, collected by a smartphone located on board a ship (Innac et al. 2021). Dardanelli and Pipitone, on the other hand, showed the availability of Continuosly Operating Reference Station (CORS) to understand the effects of a GNSS CORS network geometry and differential corrections on the solutions. The analysis is carried out using ten different network configurations, with different inter-distances between the stations within GNSMART Geo++ software. Different surveys have been performed, including four separate session tests, with acquisitions of one hour each. The analysis has been carried out using the traditional network solutions, such as the Virtual Reference Station (VRS), the Flächen Korrektur Parameter (FKP), the nearest (Near) and the farest (Far) stations. Results confirmed the great reliability of the GNSS network, with centimetre precision in terms of coordinates (North, East and Ellipsoidal Height), whether changing the geometric configuration of the network or the corrections (Dardanelli and Pipitone, 2021).Three papers on GIS were also presented in the Special Issue, and particularly remarkable is the one of Pesaresi et al. in wich after a contextualization about the measures used to contain the COVID-19 diffusion and the need to promote geotechnological proposals, data sharing and homogenous centralised systems for data collection and analysis. Successively, was presented the "Dynamic Space-Time Diffusion Simulator in a GIS Environment to Tackle the COVID-19 Emergency" that was elaborated on the basis of the data provided by the UOC Hygiene and Public Health Service -Local Health Unit Rome 1 (Pesaresi et al. 2021). Also Baiocchi et al. tested on a real case (the May 1999 pyroclastic flows in Campania, southern Italy) the actual possibility of implementing a model for forecasting such events using only open-source software and open data by usign GIS and Web GIS. It has been demonstrated that the entire process can be carried out using only open-source resources and it has ...

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“…Traditional CPU-based algorithms are very inefficient when processing tens of gigabytes or even hundreds of gigabytes, and often cannot meet real-world demands. There is also an urgent need for automated processing [44,45]. In order to achieve near real-time automatic sub-pixel registration of PAN and MS images, our method maps parallel algorithms to a GPU for processing.…”

Section: Introductionmentioning

confidence: 99%

Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

et al. 2021

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This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.

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Automation of Pan-Sharpening Methods for Pléiades Images Using GIS Basic Functions

Cited by 19 publications

References 68 publications

Comparison of Different Pan-Sharpening Methods Applied to Ikonos Imagery

Comparison of Different Pan-Sharpening Methods Applied to Ikonos Imagery

Geomatics Application for Geography

Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

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