Multi-Object Detection and Feature Extraction of Remote Sensing 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: 30 January 2026 | Viewed by 98
Special Issue Editors
Interests: deep learning; automatic target recognition; infrared image processing; remote sensing target recognition
Interests: infrared image processing; infrared polarization imaging; automatic target recognition; target tracking
Interests: clutter modeling; compressed sensing; automatic target recognition; statistical analysis
Interests: remote sensing image processing; computer vision; automation target recognition
Special Issue Information
Dear Colleagues,
Multi-object detection and feature extraction technologies are essential for conducting quantitative analysis and the intelligent interpretation of remote sensing imagery, a critical data source for Earth observation. Multi-object detection, which is responsible for accurately localizing and categorizing multiple targets within complex scenes, ranging from motor vehicles and crowds to other natural or artificial objects, faces formidable challenges. These challenges include varying target scales, background clutter, and spectral confusion. To address these challenges, the integration of advanced algorithms, such as deep learning-based region proposal networks and attention mechanisms, is essential in order to enhance detection precision and robustness. Concurrently, feature extraction, as the foundational step, focuses on distilling discriminative representations from raw imagery. These representations include spectral, spatial, textural, and contextual features. Recent advancements in transformer-based architectures and multi-modal fusion techniques have significantly facilitated the capture of high-level semantic information, thereby bridging the gap between low-level data and high-level decision-making. The synergy between multi-object detection and feature extraction underpins a wide range of applications, including environmental monitoring, urban planning, and precision agriculture. Moreover, it drives the evolution of remote sensing towards higher automation and intelligence. Given the unique characteristics of remote sensing data, such as large scene sizes and limited labeled samples, the continuous exploration of domain-specific adaptation strategies is necessary to fully realize the potential of these techniques.
This Special Issue explores advances in multi-object detection and feature extraction for remote sensing images across diverse scenarios. Focusing on deep learning, transformer architectures, and multi-modal fusion, we aim to enhance feature representation and improve detection accuracy in complex environments, aligning with this journal’s mission to promote innovation in remote sensing technology. By publishing theoretically novel and practically significant research, we seek to support the intelligent automation of remote sensing in key applications like environmental monitoring, smart cities, and precision agriculture, advancing both the science and real-world impact of the field.
Topics may range from innovations in classical object detection or feature extraction techniques to the development of novel algorithmic frameworks. We particularly welcome research addressing key challenges such as varying target scales, background clutter, and feature extraction/fusion.
We look forward to receiving your contributions.
Dr. Yu Zhang
Prof. Dr. Yan Zhang
Dr. Zhiguang Shi
Dr. Ruigang Fu
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 imagery
- multi-object detection
- feature extraction
- deep learning
- transformer-based architectures
- multi-modal fusion
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