MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery

Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano; Garzelli, Andrea; Selva, Massimo

doi:10.14358/pers.72.5.591

Cited by 733 publications

(444 citation statements)

References 11 publications

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“…Clearly, the whole procedure rests upon the hypothesis that performance does not depend critically on scale, which is not always the case, as discussed in the Introduction. Therefore, to reduce the possible mismatches, we smooth data before downsampling, following [2,22], using a filter that matches the modulation transfer function of the sensor. Likewise, we upsample the MS component using the interpolation kernel proposed in [13].…”

Section: Basic Architecturementioning

confidence: 99%

“…To obtain better coregistered upscaled images, [18] uses the Induction scaling technique, followed by a new fusion technique called Indusion. Other methods following the same philosophy work in a MRA setting, e.g., ATWT [15,[19][20][21] or with LP representations [16,[22][23][24]. Some recent papers, to avoid improper modeling, recast the problem in an optimization framework.…”

Section: Introductionmentioning

confidence: 99%

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Pansharpening by Convolutional Neural Networks

et al. 2016

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Abstract:A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

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Section: Basic Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pansharpening by Convolutional Neural Networks

et al. 2016

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“…The names and gain injection equations of the proposed methods are shown in Table 3. The proposed methods were compared with five widely-used algorithms in the literature: WATFRAC [5], BT [16], IHS [13], GS [15] and MTF-GLP [28]. Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…The main contribution of this new methodology is to produce artifact-free high spatial resolution pansharpened images, improving the MS edges by becoming aware of the scale (scale-aware) in images with a predominantly agricultural landscape. The performance of this method was compared with the WATFRAC, BT, GS, IHS and MTF-GLP (MTF using generalized Laplacian pyramid) [28] algorithms and evaluated using a set of spectral, spatial and overall quality indices [7,8]. The set of quality indices was summarized and compared using the Borda count (BC) method [29,30].…”

Section: Introductionmentioning

confidence: 99%

Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes

et al. 2016

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Abstract:Remote sensing (RS) has played an important role in extensive agricultural monitoring and management for several decades. However, the current spatial resolution of satellite imagery does not have enough definition to generalize its use in highly-fragmented agricultural landscapes, which represents a significant percentage of the world's total cultivated surface. To characterize and analyze this type of landscape, multispectral (MS) images with high and very high spatial resolutions are required. Multi-source image fusion algorithms are normally used to improve the spatial resolution of images with a medium spatial resolution. In particular, pansharpening (PS) methods allow one to produce high-resolution MS images through a coherent integration of spatial details from a panchromatic (PAN) image with spectral information from an MS. The spectral and spatial quality of source images must be preserved to be useful in RS tasks. Different PS strategies provide different trade-offs between the spectral and the spatial quality of the fused images. Considering that agricultural landscape images contain many levels of significant structures and edges, the PS algorithms based on filtering processes must be scale-aware and able to remove different levels of detail in any input images. In this work, a new PS methodology based on a rolling guidance filter (RGF) is proposed. The main contribution of this new methodology is to produce artifact-free pansharpened images, improving the MS edges with a scale-aware approach. Three images have been used, and more than 150 experiments were carried out. An objective comparison with widely-used methodologies shows the capability of the proposed method as a powerful tool to obtain pansharpened images preserving the spatial and spectral information.

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“…The MRA-based fusion techniques adopt multi-resolution analysis methods, such as multi-resolution wavelet [5,[49][50][51][52][53] and the Laplacian pyramid [54,55], to decompose multispectral and panchromatic images with different levels. They subsequently derive the spatial details that are imported into the finer scales of the multispectral images with respect to the relationship between the panchromatic and multispectral images in the coarser scales [5,54,56,57].…”

Section: A Short Overview Of Existing Pan-sharpening Algorithmsmentioning

confidence: 99%

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

2014

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Pan-sharpening algorithms are data-and computation-intensive, and the processing performance can be poor if common serial processing techniques are adopted. This paper presents a parallel computing paradigm for pan-sharpening algorithms based on a generalized fusion model and parallel computing techniques. The developed modules, including eight typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. The experiments demonstrate that if parallel strategies are adopted, in the best cases the fastest times required to finish the entire fusion operation (including disk input/output (I/O) and computation) are close to the time required to directly read and write the images without any computation. The parallel processing implemented on a workstation with two CPUs is able to perform these operations up to 13.9 times faster than serial execution. An algorithm in the framework is 32.6 times faster than the corresponding version in the ERDAS IMAGINE software. Additionally, no obvious differences in the fusion effects are observed between the fusion results of different implemented versions.

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MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery

Cited by 733 publications

References 11 publications

Pansharpening by Convolutional Neural Networks

Pansharpening by Convolutional Neural Networks

Scale-Aware Pansharpening Algorithm for Agricultural Fragmented Landscapes

A Parallel Computing Paradigm for Pan-Sharpening Algorithms of Remotely Sensed Images on a Multi-Core Computer

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