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The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 920

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China
Interests: machine learning; remote sensing; image processing

Special Issue Information

Dear Colleagues,

Machine learning has emerged as a powerful tool in remote sensing, enabling the analysis and interpretation of large-scale and complex datasets with remarkable accuracy and efficiency. By leveraging statistical theory, learning theory, and neural networks techniques, machine learning methods can automatically learn patterns and relationships within remote sensing data, uncovering hidden information and aiding in the understanding of various phenomena.

We are pleased to announce the Special Issue, “The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling”, which will provide researchers with the opportunity to present the modelling techniques of machine learning for remote sensing data analysis, also encouraging machine learning theoretical research for remote sensing data analysis. Articles for this Special Issue may address, but are not limited to, the following topics in remote sensing images:

  • Image Classification;
  • Image Clustering;
  • Image Denoising;
  • Objective Detection/Object Tracking;
  • Change Detection/Anomaly Detection;
  • Machine learning theory for RS Data: Generalizability, Interpretability, etc.

Dr. Rong Wang
Prof. Dr. Lefei Zhang
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

  • machine learning
  • image processing
  • remote sensing
  • data fusion
  • satellite images
  • aerial images

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Published Papers (1 paper)

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Research

20 pages, 5518 KiB  
Article
Hierarchical Prototype-Aligned Graph Neural Network for Cross-Scene Hyperspectral Image Classification
by Danyao Shen, Haojie Hu, Fang He, Fenggan Zhang, Jianwei Zhao and Xiaowei Shen
Remote Sens. 2024, 16(13), 2464; https://doi.org/10.3390/rs16132464 - 5 Jul 2024
Viewed by 556
Abstract
The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning [...] Read more.
The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks. Full article
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