Advanced Object Detection, Classification and Recognition in VIR Optical and SAR Remote Sensing Imagery
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 2026 | Viewed by 50
Special Issue Editors
Interests: object detection; object tracking; change detection; super-resolution; image enhancement
Interests: space infrared spectral imaging; high-resolution infrared imaging; computational spectral imaging; weak signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: object detection; change detection; image processing
Special Issue Information
Dear Colleagues,
Remote sensing imagery has matured into an indispensable data source for characterizing and monitoring Earth’s surface dynamics. With the advent of ultra-high-resolution commercial satellite constellations and unmanned aerial vehicles (UAVs), the value of spaceborne and airborne optical and synthetic aperture radar (SAR) data in environmental research is now well established. Continuous advances in detector sensitivity, radiometric calibration and platform stability have significantly expanded the applicability of visible-to-infrared (VIR) and SAR imagery. Nevertheless, accurate interpretation of raw VIR and SAR images for object detection, classification and recognition remains challenging due to the complex and diverse nature of remote sensing targets and imaging conditions. Consequently, accurate object detection, classification and recognition in remote sensing imagery have become critically dependent on advanced techniques from diverse fields, including signal processing, machine learning and optimization strategies, to overcome image quality degradation caused by various factors. Moreover, high-level tasks including semantic segmentation, object detection and change detection are increasingly transitioning from handcrafted feature-based methods to deep-learning architectures, enabling automated, scalable and precise analysis of remote sensing data across diverse application domains.
This Special Issue aims to gather cutting-edge research that spans the full spectrum of VIR optical and SAR image analysis, from low-level image processing to high-level object detection, classification and recognition. Contributions may address pixel-level or object-level targets and are encouraged to focus on these core tasks within remote sensing imagery.Particular emphasis is placed on deep-learning methods that operate across multiple spatial, spectral, spatiotemporal and frequency domains, supporting tasks such as object detection, tracking, semantic segmentation and so on.
Article topics may include, but are not limited to, the following:
- Visible and infrared remote sensing image object detection;
- SAR target detection;
- Small object detection;
- Dense small target detection and tracking;
- Arbitrarily oriented object detection;
- Single- and multi-object tracking in VIR or SAR image sequences;
- Open-set, zero-shot and few-shot object recognition;
- Template matching and feature-based target recognition;
- Multi-scale, multi-domain feature learning;
- Joint VIR–SAR analysis for robust object detection and recognition;
- Multispectral image processing and analysis for target detection;
- Hyperspectral image understanding and sub-pixel target recognition.
Dr. Yuan Liu
Prof. Dr. Chunlai Li
Dr. Dongdong Pang
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 250 words) can be sent to the Editorial Office for assessment.
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
- object detection
- image classification
- small object detection
- deep learning
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