A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening

Vitale, Sergio; Scarpa, Giuseppe

doi:10.3390/rs12030348

Cited by 44 publications

(18 citation statements)

References 64 publications

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“…A very traditional form of super-resolution transformation is pan-sharpening where the panchromatic data is employed to increase the resolution of MSI or HSI data. Different studies show that deep learning techniques outperform conventional pan-sharpening approaches by automatic extraction of features that indicate the correlations between the two data types [153][154][155][156].…”

Section: Multi-modal Data Fusionmentioning

confidence: 99%

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

2020

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Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

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Section: Multi-modal Data Fusionmentioning

confidence: 99%

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

2020

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“…In fact, the widely used training of pansharpening networks follow Wald's protocol and its implementation is the same as the one used to simulate reduced resolution datasets. However, new research lines (see, e.g., [27], [28]) are proposing new training procedures for pansharpening networks by directly exploiting original (full resolution) MS and PAN data.…”

Section: A Data Pre-processingmentioning

confidence: 99%

A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

Vivone

Mura

Garzelli

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Comparative evaluation is a requirement for reproducible science and objective assessment of new algorithms. Reproducible research in the field of pansharpening of very high resolution images is a difficult task due to the lack of openly available reference datasets and protocols. The contribution of this work is three-fold and it defines a benchmarking framework to evaluate pansharpening algorithms. First, it establishes a reference dataset, named PAirMax, composed of 14 panchromatic and multispectral image pairs collected over heterogeneous landscapes by different satellites. Second, it standardizes various image pre-processing steps, such as filtering, upsampling, and band co-registration, by providing a reference implementation. Third, it details the quality assessment protocols for reproducible algorithm evaluation.

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“…The main issue of the above-mentioned ML approaches is the assumption of a training paradigm relying on a resolution downgrade process (e.g., Wald's protocol). More recently, different training paradigms, mainly based on multi-objective strategies, such as in [62,63], have been proposed to address such a problem.…”

Section: Introductionmentioning

confidence: 99%

An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening

2021

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The spatial resolution of multispectral data can be synthetically improved by exploiting the spatial content of a companion panchromatic image. This process, named pansharpening, is widely employed by data providers to augment the quality of images made available for many applications. The huge demand requires the utilization of efficient fusion algorithms that do not require specific training phases, but rather exploit physical considerations to combine the available data. For this reason, classical model-based approaches are still widely used in practice. We created and assessed a method for improving a widespread approach, based on the generalized Laplacian pyramid decomposition, by combining two different cost-effective upgrades: the estimation of the detail-extraction filter from data and the utilization of an improved injection scheme based on multilinear regression. The proposed method was compared with several existing efficient pansharpening algorithms, employing the most credited performance evaluation protocols. The capability of achieving optimal results in very different scenarios was demonstrated by employing data acquired by the IKONOS and WorldView-3 satellites.

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A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening

Cited by 44 publications

References 64 publications

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

An Improved Version of the Generalized Laplacian Pyramid Algorithm for Pansharpening

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