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Remote Sensing
Special Issues
Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing
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Special Issue "Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing"

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1328

Special Issue Editors

Prof. Dr. Junmin Liu

E-Mail Website
Guest Editor
Department of Information Science, Xi’an Jiaotong University, Xi’an 710049, China
Interests: machine learning; hyperspectral unmixing of remote sensing images; remote sensing image fusion; data mining; intelligent computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xile Zhao
E-Mail Website
Guest Editor
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610051, China
Interests: hyperspectral image processing; machine learning; scientific computing
Prof. Dr. Tieyong Zeng

E-Mail Website
Guest Editor
Department of Mathematics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong
Interests: image processing; optimization; artificial intelligence; scientific computing; computer vision; machine learning; inverse problems
Dr. Bin Zhao
E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavík, Iceland
Interests: hyperspcetral image processing; machine learning
Dr. Claudia Paris

E-Mail Website1 Website2
Guest Editor
Remote Sensing Laboratory (RsLab), Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 5, Povo, I-38123 Trento, Italy
Interests: remote sensing; image processing; signal processing; pattern recognition; classification and fusion of multisource remote sensing data; multi-temporal image analysis; biophysical parameter estimation

Special Issue Information

Dear Colleagues,

Remote sensing image processing plays a critical role in diverse fields such as environmental monitoring, resource management, and disaster response. However, processing and analyzing remotely sensed data can be challenging due to complex environments, limited signal-to-noise ratio, and the presence of noise and artifacts. Recently, two different approaches to remote sensing image processing have emerged: knowledge-driven and data-driven methods. Among these, the knowledge-driven methods, based on expert experience or mathematical models describing the physical processes underlying remote sensing data, show high interpretability. In contrast, data-driven methods leverage machine learning algorithms to identify correlations and patterns from observed data, which are prevalent in recent years. In particular, this Special Issue focuses on exploring the advantages and limitations of knowledge-driven and data-driven approaches and suggesting ways to combine them to boost remote sensing image processing. We are looking forward to receiving a variety of works on this topic, whether they are theoretical or heuristic. This Special Issue is expected to leverage the strengths of knowledge-driven and data-driven methods and provide valuable insights into developing better remote sensing techniques for a broad range of applications.

Topics of interest include, but are not limited to, the following points:

  • General remote sensing image processing, such as classification, object detection, segmentation, super-resolution, denoising, etc.
  • Real-world applications based on remote sensing images, such as land use mapping, vegetation analysis, and environmental monitoring.
  • Combining traditional methods and deep learning methods for remote sensing image processing and analysis.
  • Multi-modal remote sensing image processing, such as multi-modal image fusion, pan-sharpening, etc.

Prof. Dr. Junmin Liu
Prof. Dr. Xile Zhao
Prof. Dr. Tieyong Zeng
Dr. Bin Zhao
Dr. Claudia Paris
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

  • image processing
  • remote sensing
  • knowledge-driven methods
  • data-driven methods

Published Papers (2 papers)

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Research

25 pages, 5631 KiB  
Article
Learn by Yourself: A Feature-Augmented Self-Distillation Convolutional Neural Network for Remote Sensing Scene Image Classification
by
Cuiping Shi
,
Mengxiang Ding
,
Liguo Wang
and
Haizhu Pan
Remote Sens. 2023, 15(23), 5620; https://doi.org/10.3390/rs15235620 - 04 Dec 2023
Viewed by 318
Abstract
In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which [...] Read more.
In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which makes it difficult for commonly used networks to effectively learn the features of remote sensing scene images. In addition, most existing methods adopt hard labels to supervise the network model, which makes the model prone to losing fine-grained information of ground objects. In order to solve these problems, a feature-augmented self-distilled convolutional neural network (FASDNet) is proposed. First, ResNet34 is adopted as the backbone network to extract multi-level features of images. Next, a feature augmentation pyramid module (FAPM) is designed to extract and fuse multi-level feature information. Then, auxiliary branches are constructed to provide additional supervision information. The self-distillation method is utilized between the feature augmentation pyramid module and the backbone network, as well as between the backbone network and auxiliary branches. Finally, the proposed model is jointly supervised using feature distillation loss, logits distillation loss, and cross-entropy loss. A lot of experiments are conducted on four widely used remote sensing scene image datasets, and the experimental results show that the proposed method is superior to some state-ot-the-art classification methods. Full article
(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)
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17 pages, 65010 KiB  
Article
Remote Sensing Image Super-Resolution via Multi-Scale Texture Transfer Network
by
Yu Wang
,
Zhenfeng Shao
,
Tao Lu
,
Xiao Huang
,
Jiaming Wang
,
Xitong Chen
,
Haiyan Huang
and
Xiaolong Zuo
Remote Sens. 2023, 15(23), 5503; https://doi.org/10.3390/rs15235503 - 25 Nov 2023
Viewed by 503
Abstract
As the degradation factors of remote sensing images become increasingly complex, it becomes challenging to infer the high-frequency details of remote sensing images compared to ordinary digital photographs. For super-resolution (SR) tasks, existing deep learning-based single remote sensing image SR methods tend to [...] Read more.
As the degradation factors of remote sensing images become increasingly complex, it becomes challenging to infer the high-frequency details of remote sensing images compared to ordinary digital photographs. For super-resolution (SR) tasks, existing deep learning-based single remote sensing image SR methods tend to rely on texture information, leading to various limitations. To fill this gap, we propose a remote sensing image SR algorithm based on a multi-scale texture transfer network (MTTN). The proposed MTTN enhances the texture feature information of reconstructed images by adaptively transferring texture information according to the texture similarity of the reference image. The proposed method adopts a multi-scale texture-matching strategy, which promotes the transmission of multi-scale texture information of remote sensing images and obtains finer-texture information from more relevant semantic modules. Experimental results show that the proposed method outperforms state-of-the-art SR techniques on the Kaggle open-source remote sensing dataset from both quantitative and qualitative perspectives. Full article
(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)
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