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Remote Sensing
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Pan-Sharpening Methods for Remotely Sensed Images
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Pan-Sharpening Methods for Remotely Sensed Images

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3804

Special Issue Editors

Dr. Paolo Addesso

E-Mail Website
Guest Editor
DIEM, University of Salerno, 84084 Fisciano, Italy
Interests: remote sensing; image processing; signal processing; sequential Bayesian estimation; estimation theory; detection theory; statistical signal processing; fractal models; data fusion; gravitational waves; localization; nonlinear devices; sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Rocco Restaino

E-Mail Website
Guest Editor
DIEM, University of Salerno, 84084 Fisciano, Italy
Interests: probability theory; stochastic geometry; data fusion and signal processing for remote sensing and networking

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the fusion of data acquired by multiple remote sensors for the construction of high quality synthetic images. Most applications of remote sensing require images characterized by optimal features in different domains, conflicting with the capabilities of acquisition, storage and transmission apparatuses. Data fusion techniques allow us to overcome technological limitations by combining data with complementary characteristics.

Pan-sharpening methods constitute a successful application of this methodology, which stimulated the development of a large class of algorithms exploitable for combining different kinds of data. In addition to optical multispectral and hyperspectral data, these fusion techniques apply to thermal data, as well as to the fusion of heterogeneous data acquired by sensors of different nature, such as RADAR or LiDAR. The utilization is not limited to single pairs of images, but also includes sequences of images or videos.

The application field is very wide, embracing the accurate representation of the Earth surface for human visual interpretation, object recognition, land cover analysis and monitoring, agriculture, archeology, soil moisture estimation, water resource management, digital elevation and surface models construction, and so on.

A non-exclusive list of topics of particular interest for this issue is the following.

  • Remotely sensed data combinations:

- Multispectral and panchromatic;

- Hyperspectral sharpening using multispectral and/or panchromatic data;

- Thermal and short-wave images and sequences;

- Multimodal data fusion: optical, thermal, SAR, LiDAR;

  • Image fusion approaches:

- Component Substitution and Multiresolution Analysis approaches;

- Morphological techniques;

- Variational Optimization;

- Machine-Learning;

  • Image fusion models:

- Physical models of signal propagation and acquisition;

- Deterministic and statistical models;

- Decision strategies;

  • Applications of image fusion:

- Target object identification, detection and tracking;

- Land cover mapping and monitoring;

- Image segmentation;

- Precision agriculture;

- Soil moisture estimation;

- Water resource management;

- Continuous monitoring of the sea;

- Innovative applications;

  • Assessment of fused images quality:

- Indexes for image quality evaluation;

- Protocols for data fusion techniques assessment.

Dr. Paolo Addesso
Dr. Rocco Restaino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing 
  • data fusion
  • pansharpening
  • hyperspectral sharpening
  • spatio-temporal data fusion
  • machine learning for data fusion
  • multispectral sensors
  • thermal sensors
  • SAR
  • LiDAR

Published Papers (1 paper)

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Research

24 pages, 5110 KiB  
Article
Pan-Sharpening Based on CNN+ Pyramid Transformer by Using No-Reference Loss
by Sijia Li, Qing Guo and An Li
Remote Sens. 2022, 14(3), 624; https://doi.org/10.3390/rs14030624 - 27 Jan 2022
Cited by 18 | Viewed by 2943
Abstract
The majority of existing deep learning pan-sharpening methods often use simulated degraded reference data due to the missing of real fusion labels which affects the fusion performance. The normally used convolutional neural network (CNN) can only extract the local detail information well which [...] Read more.
The majority of existing deep learning pan-sharpening methods often use simulated degraded reference data due to the missing of real fusion labels which affects the fusion performance. The normally used convolutional neural network (CNN) can only extract the local detail information well which may cause the loss of important global contextual characteristics with long-range dependencies in fusion. To address these issues and to fuse spatial and spectral information with high quality information from the original panchromatic (PAN) and multispectral (MS) images, this paper presents a novel pan-sharpening method by designing the CNN+ pyramid Transformer network with no-reference loss (CPT-noRef). Specifically, the Transformer is used as the main architecture for fusion to supply the global features, the local features in shallow CNN are combined, and the multi-scale features from the pyramid structure adding to the Transformer encoder are learned simultaneously. Our loss function directly learns the spatial information extracted from the PAN image and the spectral information from the MS image which is suitable for the theory of pan-sharpening and makes the network control the spatial and spectral loss simultaneously. Both training and test processes are based on real data, so the simulated degraded reference data is no longer needed, which is quite different from most existing deep learning fusion methods. The proposed CPT-noRef network can effectively solve the huge amount of data required by the Transformer network and extract abundant image features for fusion. In order to assess the effectiveness and universality of the fusion model, we have trained and evaluated the model on the experimental data of WorldView-2(WV-2) and Gaofen-1(GF-1) and compared it with other typical deep learning pan-sharpening methods from both the subjective visual effect and the objective index evaluation. The results show that the proposed CPT-noRef network offers superior performance in both qualitative and quantitative evaluations compared with existing state-of-the-art methods. In addition, our method has the strongest generalization capability by testing the Pleiades and WV-2 images on the network trained by GF-1 data. The no-reference loss function proposed in this paper can greatly enhance the spatial and spectral information of the fusion image with good performance and robustness. Full article
(This article belongs to the Special Issue Pan-Sharpening Methods for Remotely Sensed Images)
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