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Reviews in Remote Sensing Image Processing: Methods, Architectures, and Applications

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 665

Special Issue Editor


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Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, P.O. Box 64, Xi’an 710072, China
Interests: remote sensing; image analysis; computer vision; pattern recognition; machine learning
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Special Issue Information

Dear Colleagues,

Remote sensing image processing plays a pivotal role in transforming raw Earth observation data into actionable geospatial intelligence. With the rapid proliferation of satellite platforms, UAV systems, and ground-based sensors, the volume, variety, and velocity of remotely sensed data have grown exponentially. Effective processing techniques are therefore essential to extract meaningful information, reduce noise, align multi-source data, and enable accurate interpretation in both scientific and operational contexts.

This Special Issue is dedicated exclusively to high-quality review papers that provide in-depth surveys of remote sensing processing techniques. The goal is to offer a clear and structured overview of the State of the Art, including the comparative evaluation of algorithms, methodological taxonomies, technological developments, and application-driven innovations. Such reviews will serve as essential references for both new researchers entering the field and established scholars aiming to identify gaps and future research directions.

  • Image enhancement and restoration;
  • Remote sensing image classification and segmentation;
  • Optical/hyperspectral/multispectral/infrared/radar/sonar image analysis;
  • Remote sensing object-based image understanding;
  • Object/change/anomaly detection;
  • Geospatial image fusion;
  • Multi-modal/multi-sensor/multi-temporal data fusion;
  • Machine learning for remote sensing image processing;
  • Image augmentation techniques;
  • Image registration, stitching, and georeferencing;
  • Remote sensing image compression.

Prof. Dr. Qi Wang
Guest Editor

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

  • image processing
  • remote sensing
  • review
  • survey

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Published Papers (1 paper)

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Review

35 pages, 777 KB  
Review
Predictive Autonomy for UAV Remote Sensing: A Survey of Video Prediction
by Zhan Chen, Enze Zhu, Zile Guo, Peirong Zhang, Xiaoxuan Liu, Lei Wang and Yidan Zhang
Remote Sens. 2025, 17(20), 3423; https://doi.org/10.3390/rs17203423 - 13 Oct 2025
Viewed by 313
Abstract
The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust [...] Read more.
The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs. Full article
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