Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing Image Classification: Theory and Application
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Image Classification: Theory and Application

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 571

Special Issue Editors

Dr. Han Zhai

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: remote sensing image processing; remote sensing information extraction and application
Dr. Yuxiang Zhang

E-Mail Website
Guest Editor
School of Geographies and Geomatics, China University of Geosciences, Wuhan, China
Interests: remote sensing classification; change detection and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of earth observation technology, remote sensing has shown increasing superiority in various applications, allowing for a clear record of the appearance and spatial layout of different ground objects to support refined information extraction. Classification is an effective technique for remote sensing image interpretation and analysis and has become the foundation of a variety of applications, including precision agriculture, fine forestry, wetland monitoring, mineral exploration, and so on. In the past decades, a large amount of remote sensing image classification methods has been proposed, and extensive relevant applications have been carried out to obtain numerous meaningful findings and conclusions, which have greatly promoted the development of remote sensing technology. Thus, we have organized a Special Issue entitled “Remote Sensing Image Classification: Theory and Application”. Contributions using new theories, methods, and relevant interesting applications are especially welcome.

Dr. Han Zhai
Dr. Yuxiang 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

  • remote sensing image
  • artificial intelligence
  • deep learning
  • classification
  • clustering
  • change detection
  • applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 2146 KiB  
Article
Large-Scale Hyperspectral Image-Projected Clustering via Doubly Stochastic Graph Learning
by Nian Wang, Zhigao Cui, Yunwei Lan, Cong Zhang, Yuanliang Xue, Yanzhao Su and Aihua Li
Remote Sens. 2025, 17(9), 1526; https://doi.org/10.3390/rs17091526 - 25 Apr 2025
Viewed by 93
Abstract
Hyperspectral image (HSI) clustering has drawn more and more attention in recent years as it frees us from labor-intensive manual annotation. However, current works cannot fully enjoy the rich spatial and spectral information due to redundant spectral signatures and fixed anchor learning. Moreover, [...] Read more.
Hyperspectral image (HSI) clustering has drawn more and more attention in recent years as it frees us from labor-intensive manual annotation. However, current works cannot fully enjoy the rich spatial and spectral information due to redundant spectral signatures and fixed anchor learning. Moreover, the learned graph always obtains suboptimal results due to the separate affinity estimation and graph symmetry. To address the above challenges, in this paper, we propose large-scale hyperspectral image-projected clustering via doubly stochastic graph learning (HPCDL). Our HPCDL is a unified framework that learns a projected space to capture useful spectral information, simultaneously learning a pixel–anchor graph and an anchor–anchor graph. The doubly stochastic constraints are conducted to learn an anchor–anchor graph with strict probabilistic affinity, directly providing anchor cluster indicators via connectivity. Meanwhile, when using label propagation, pixel-level clustering results are obtained. An efficient optimization strategy is proposed to solve our HPCDL model, requiring monomial linear complexity concerning the number of pixels. Therefore, our HPCDL has the ability to deal with large-scale HSI datasets. Experiments on three datasets demonstrate the superiority of our HPCDL for both clustering performance and the time burden. Full article
(This article belongs to the Special Issue Remote Sensing Image Classification: Theory and Application)
►▼ Show Figures

Graphical abstract

30 pages, 20114 KiB  
Article
Multi-Feature Lightweight DeeplabV3+ Network for Polarimetric SAR Image Classification with Attention Mechanism
by Junfei Shi, Shanshan Ji, Haiyan Jin, Yuanlin Zhang, Maoguo Gong and Weisi Lin
Remote Sens. 2025, 17(8), 1422; https://doi.org/10.3390/rs17081422 - 16 Apr 2025
Viewed by 181
Abstract
Polarimetric Synthetic Aperture Radar (PolSAR) is an advanced remote sensing technology that provides rich polarimetric information. Deep learning methods have been proved an effective tool for PolSAR image classification. However, relying solely on source data input makes it challenging to effectively classify all [...] Read more.
Polarimetric Synthetic Aperture Radar (PolSAR) is an advanced remote sensing technology that provides rich polarimetric information. Deep learning methods have been proved an effective tool for PolSAR image classification. However, relying solely on source data input makes it challenging to effectively classify all land cover targets, especially heterogeneous targets with significant scattering variations, such as urban areas and forests. Besides, multiple features can provide more complementary information, while feature selection is crucial for classification. To address these issues, we propose a novel attention mechanism-based multi-feature lightweight DeeplabV3+ network for PolSAR image classification. The proposed method integrates feature extraction, learning, selection, and classification into an end-to-end network framework. Initially, three kinds of complementary features are extracted to serve as inputs to the network, including polarimetric original data, statistical and scattering features, textural and contour features. Subsequently, a lightweight DeeplabV3+ network is designed to conduct multi-scale feature learning on the extracted multidimensional features. Finally, an attention mechanism-based feature selection module is integrated into the network model, adaptively learning weights for multi-scale features. This enhances discriminative features but suppresses redundant or confusing features. Experiments are conducted on five real PolSAR data sets, and experimental results demonstrate the proposed method can achieve more precise boundaries and smoother regions than the state-of-the-art algorithms. In this paper, we develop a novel multi-feature learning framework, achieving a fast and effective classification network for PolSAR images. Full article
(This article belongs to the Special Issue Remote Sensing Image Classification: Theory and Application)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop