Remote Sensing Data Dense Prediction and Feature Extraction
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 October 2026 | Viewed by 155
Special Issue Editors
Interests: computer vision; multi-modal data processing; remote sensing data processing; saliency detection; object/semantic segmentation; defect inspection
Interests: machine learning; meta-learning and domain adaptation; with applications in image classification; gaze estimation; point cloud; and remote sensing
Interests: low-level computer vision including AIGC and detection; text-to-image/video; image/video restoration; compression and multi-modal models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid advancement of sensor technologies, the volume and complexity of remote sensing (RS) data ranging from high resolution optical imagery to multi-source heterogeneous data have grown exponentially. Extracting meaningful information from these complex datasets requires robust and efficient computational frameworks. Dense prediction, a critical paradigm in computer vision, has emerged as a cornerstone in RS applications. Unlike scene level classification, dense prediction tasks such as semantic segmentation, saliency detection and change detection require pixel level or point level precision, which is essential for accurate land cover mapping, urban planning and environmental monitoring.
The aim of this Special Issue is to collect innovative research that addresses the challenges of high precision feature extraction and dense prediction in the remote sensing. We seek contributions that leverage cutting edge techniques including deep learning, foundation models, and generative AI to enhance the interpretability and efficiency of RS data processing. This Special Issue aligns perfectly with the scope of Remote Sensing by fostering the development of advanced image processing algorithms and automated interpretation methods for earth observation. We particularly encourage submissions that tackle the unique challenges of RS data such as drastic scale variations, complex backgrounds and limited high quality annotations.
We invite original research articles, comprehensive reviews and technical notes covering the following themes:
- Remote Sensing Data Segmentation: New architectures for semantic, instance and panoptic segmentation.
- Saliency and Change Detection: Identifying prominent objects or temporal variations in complex RS scenes.
- Object Detection and Classification: High precision localization and categorization of multi-scale targets.
- Advanced Feature Extraction: Novel descriptors for structural, spectral and temporal information.
- Remote Sensing Data Generation: Applications of diffusion models and GANs for data augmentation and virtual scene synthesis.
- Low-level Processing and Restoration: State-of-the-art methods for RS image inpainting, denoising, dehazing and super-resolution.
- Multi-modal and Cross-domain Learning: Feature fusion across SAR, optical and LiDAR data.
Dr. Gongyang Li
Dr. Yong Wu
Dr. Feng Li
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
- segmentation
- object detection
- classification
- feature extraction
- data generation
- inpainting/denoising/dehazing
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