Deep Learning Techniques for Pixel Classification and Image Retrieval in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 43
Special Issue Editors
Interests: remote sensing; deep learning; machine learning; environmental planning; urban planning
2. OJEong Resilience Institution, Korea University, Seoul 02841, Republic of Korea
Interests: remote sensing; spatiotemporal modeling; GIS; machine learning; disaster modeling forest modeling
2. OJEong Resilience Institution, Korea University, Seoul 02841, Republic of Korea
Interests: knowledge based AI modeling; wildfire modeling; deep learning; remote sensing
Special Issue Information
Dear Colleagues,
The growing availability of high-resolution satellite imagery from diverse sources such as Sentinel, Landsat, PlanetScope, and ECOSTRESS has significantly enhanced our ability to observe and analyze the Earth’s surface at fine spatial and temporal scales. Alongside these developments, recent advances in machine learning and deep learning have enabled more sophisticated and automated approaches to extracting meaningful information from such imagery.
In particular, deep learning architectures such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Transformer-based models, and diffusion models have demonstrated remarkable performance in pixel-wise classification and content-based image retrieval tasks. In addition, super-resolution methods built upon these models have proven valuable as complementary tools, enhancing the spatial detail of remote sensing imagery and improving the accuracy of subsequent analysis. Collectively, these approaches exhibit robust capabilities and have been widely applied across various remote sensing research domains, including land cover classification and disaster assessment (e.g., flood and forest fire mapping), as well as climate and environmental monitoring.
This Special Issue aims to synthesize recent advances in methods for pixel-wise classification and image retrieval in remote sensing, with a focus on both technical innovation and applications across diverse research domains. We welcome original research articles, review papers, and technical reports that propose novel model architectures or demonstrate meaningful applications and practical implementations in pixel-wise classification and image retrieval. Relevant topics include, but are not limited to, the following areas:
- Pixel-wise classification and semantic segmentation using deep learning;
- Content-based image retrieval in remote sensing imagery;
- Benchmark datasets and evaluation methods for classification and retrieval;
- Transfer learning and self-supervised approaches for label-scarce scenarios;
- Multi-source data fusion for classification and retrieval;
- Super-resolution for enhanced classification and retrieval performance;
- Transformer-based models for classification and visual feature extraction.
Dr. Kyungil Lee
Dr. Eunbeen Park
Dr. Hyun-Woo Jo
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
- high-resolution remote sensing
- satellite imagery
- pixel-wise classification
- time-series image analysis
- machine learning applications
- deep learning applications
- multi-source feature fusion
- domain adaptation
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