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Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 4289

Special Issue Editors

Dr. Shengke Wang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Interests: computer vision; remote sensing image object detection; remote sensing image segmentation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bineng Zhong

E-Mail Website
Guest Editor
The Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China
Interests: collaborative computing; edge computing; object detection; object tracking; person re-identification; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guoqiang Zhong

E-Mail Website
Guest Editor
College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Interests: artificial intelligence; deep learning, computer vision, natural language processing, artificial intelligence oceanography
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Gao

E-Mail Website
Guest Editor
Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: hyperspectral image processing; artificial intelligence in oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Humanity has accumulated vast amounts of remote sensing data (2D and 3D) through satellite sensors, yet effectively processing and fully utilizing this information remains a challenge. The concept of computer vision involves enabling computer systems to extract meaningful information from images, videos, and other visual inputs and then taking action or providing recommendations based on this information. By combining computer vision with machine learning techniques, it becomes possible to deeply analyze scenes in remote sensing imagery, including geographical locations and thespatial distributions of objects and their relationships, significantly enhancing the accuracy and intelligence of data interpretation. Therefore, the integration of remote sensing data with computer vision is increasingly becoming a key issue in contemporary society.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about advances in imaging and sensing for analysis of 2D/3D remote sensing data in geoscience.

This Special Issue focuses on the analysis of 2D/3D remote sensing data in geoscience using computer vision technology. It includes theories and methods from the analysis of images or videos, online processing, to real-world practical applications. This can be achieved through image/signal processing or deep/machine learning algorithms. The latest technological developments will be shared through this Special Issue. Researchers and investigators are invited to contribute original research or review articles to this Special Issue.

This Special Issue will welcome manuscripts that link the following themes:

  • Analysis of remote sensing video/images (image classification, object detection, object tracking, image segmentation, feature extraction, change detection, etc.).
  • Data stitching and fusion: stitching of large-scale data from drone images and fusion with satellite, aerial, or ground data.
  • Remote sensing online and real-time processing: research on embedded platforms carried by satellites and drones.
  • Satellite and drone flight control ground software development: research into intelligent control.
  • Applications (marine monitoring, resource surveying, search and rescue, agriculture, forestry, c-urban monitoring, disaster prevention and assessment, etc.).
  • Anything related to remote sensing images is open for submission.

We look forward to receiving your original research articles and reviews.

Dr. Shengke Wang
Prof. Dr. Bineng Zhong
Prof. Dr. Guoqiang Zhong
Dr. Feng Gao
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

  • object detection and tracking
  • intelligent interpretation of remote sensing video/image
  • image segmentation and feature extraction
  • image stitching and fusion
  • online and real time processing
  • monitoring and inspection
  • application and software development

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

36 pages, 4902 KB  
Article
PFEB: A Post-Fusion Enhanced Decoder Module for Remote Sensing Semantic Segmentation
by Dongjie Lian, Gang Chen, Biao Wu and Feifan Yang
Remote Sens. 2026, 18(8), 1246; https://doi.org/10.3390/rs18081246 - 20 Apr 2026
Viewed by 245
Abstract
Remote sensing semantic segmentation is fundamental to applications such as land-cover mapping, urban analysis, and environmental monitoring. However, remote sensing scenes often exhibit pronounced scale variation, fragmented regions, dense small objects, and complex boundary transitions, making fine-grained prediction particularly challenging. Transformer-based architectures such [...] Read more.
Remote sensing semantic segmentation is fundamental to applications such as land-cover mapping, urban analysis, and environmental monitoring. However, remote sensing scenes often exhibit pronounced scale variation, fragmented regions, dense small objects, and complex boundary transitions, making fine-grained prediction particularly challenging. Transformer-based architectures such as SegFormer have demonstrated a strong capability in modeling long-range context through hierarchical encoding, yet their lightweight decoders mainly rely on linear projection and feature fusion, providing limited capacity for local refinement after multi-scale aggregation. This limitation may reduce spatial precision in boundary-sensitive and small-object-rich regions. To address this issue, we propose the Post-fusion Enhanced Block (PFEB), a lightweight decoder-side refinement module inserted after multi-scale feature fusion and before pixel-wise classification. PFEB combines channel expansion, depthwise and pointwise convolutions, efficient channel attention (ECA), and residual learning to enhance local semantic refinement while largely preserving computational efficiency. Built upon SegFormer, the proposed method was evaluated on two widely used remote sensing benchmarks, i.e., LoveDA and ISPRS Vaihingen, under both Mix Transformer-B0 (MiT-B0) and Mix Transformer-B2 (MiT-B2) backbones. Experimental results show that PFEB consistently improves the SegFormer baseline across datasets and model scales. Under MiT-B2 backbone, our method achieves 53.82 ± 0.31 mean intersection over union (mIoU) on LoveDA and 74.84 ± 0.41 mIoU on ISPRS Vaihingen. Boundary- and size-aware evaluations further indicate that the gains are mainly reflected in improved semantic correctness near boundaries and in the recoverability of small objects. With only modest additional cost (approximately +0.53 M parameters and +8.7 G floating point operations (FLOPs)), PFEB provides a favorable accuracy–efficiency trade-off. These results suggest that PFEB is an effective and lightweight post-fusion refinement module for improving fine-grained remote sensing semantic segmentation. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition))
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20 pages, 4510 KB  
Article
TiBT-Net: A High-Resolution Remote Sensing Image Change Detection Network Integrating Bi-Temporal Space Enhancement and Token Interaction
by Yihua Ni, Shengyan Liu, Tengyue Guo and Min Xia
Remote Sens. 2026, 18(5), 805; https://doi.org/10.3390/rs18050805 - 6 Mar 2026
Cited by 3 | Viewed by 490
Abstract
Remote sensing image change detection serves as a core technology in environmental monitoring. While the widespread availability of high-resolution remote sensing data provides essential support for detailed detection, it also presents technical challenges such as complex terrain interference, subtle change recognition, and large-scale [...] Read more.
Remote sensing image change detection serves as a core technology in environmental monitoring. While the widespread availability of high-resolution remote sensing data provides essential support for detailed detection, it also presents technical challenges such as complex terrain interference, subtle change recognition, and large-scale scene processing. Current mainstream deep learning methods, despite their global modeling advantages, demonstrate limitations in cross-temporal fine-grained correlation mining and are prone to ambiguous edge localization in changing areas due to spatial detail loss. This paper proposes a high-resolution change detection network (TiBT-Net) that integrates bi-temporal space enhancement with token interaction. The model achieves precise change detection through dynamic token interaction and adaptive enhancement (TDIAE), utilizing deformable attention to capture semantic correlations. It constructs a Bi-Temporal Information Interaction Module (BTII) that enhances spatial details via multi-scale convolutions and channel attention, while introducing a delayed fusion mechanism (DLF) to dynamically balance dual-branch feature contributions. Experimental validations on LEVIR-CD, WHU-CD, and DSIFN-CD datasets achieved F1 scores of 90.38%, 86.74% and 96.28%, respectively, with Intersection-Union Ratios (IoU) of 82.46%, 76.59% and 92.82%. The overall accuracy (OA) reached up to 99.04%. This model effectively resolves the integration conflict between semantic information and spatial details, providing a reliable technical solution for high-precision change detection in complex scenarios. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition))
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29 pages, 8104 KB  
Article
HW-OPINN: A Heat Wave-Optimized Physics-Informed Neural Network for Marine Heatwave Prediction
by Qi He, Ruize Bi, Wei Zhao, Wenbo Zhang, Yanling Du and Yulin Chen
Remote Sens. 2026, 18(5), 723; https://doi.org/10.3390/rs18050723 - 27 Feb 2026
Viewed by 410
Abstract
Marine heatwaves (MHWs) are prolonged extreme warming events that pose severe threats to marine ecosystems and coastal communities, necessitating reliable prediction capabilities for climate adaptation and marine resource management. Traditional numerical models, while physically grounded, are constrained by computational costs and error accumulation, [...] Read more.
Marine heatwaves (MHWs) are prolonged extreme warming events that pose severe threats to marine ecosystems and coastal communities, necessitating reliable prediction capabilities for climate adaptation and marine resource management. Traditional numerical models, while physically grounded, are constrained by computational costs and error accumulation, whereas purely data-driven approaches often lack physical consistency and generalize poorly to extreme events. To address these challenges, this study proposes a Heat Wave-Optimized Physics-Informed Neural Network (HW-OPINN) that synergistically integrates ocean mixed-layer heat budget dynamics with adaptive deep learning techniques. The proposed framework introduces three methodological innovations. First, an adaptive sampling strategy grounded in Boltzmann distribution theory dynamically reallocates physical collocation points toward high-gradient regions based on historical loss patterns. Second, a residual-based adaptive weight update mechanism automatically modulates physical constraint contributions across spatially heterogeneous regions during training. Third, a Bayesian optimization framework employing Gaussian process surrogates systematically balances physical constraints against data fitting objectives. The framework is validated through comprehensive experiments in the Mediterranean Sea using multi-source reanalysis data spanning over two decades. Results demonstrate that HW-OPINN achieves superior performance in sea surface temperature (SST) prediction, with a test MSE of 0.009138 and RMSE of 0.095595, representing improvements of 43.9% and 25.1%, respectively, compared to the ConvLSTM baseline (MSE: 0.016275, RMSE: 0.127575), and 44.8% and 25.7% improvements over standard PINN (MSE: 0.016550, RMSE: 0.128661). Based on the predicted SST fields, the model successfully reproduces the spatial heterogeneity of key MHW characteristics, including event frequency, duration, and intensity distributions, demonstrating its effectiveness for downstream MHW detection and analysis. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition))
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33 pages, 2435 KB  
Article
Multi-Task Learning for Ocean-Front Detection and Evolutionary Trend Recognition
by Qi He, Anqi Huang, Lijia Geng, Wei Zhao and Yanling Du
Remote Sens. 2025, 17(23), 3862; https://doi.org/10.3390/rs17233862 - 28 Nov 2025
Viewed by 632
Abstract
Ocean fronts are central to upper-ocean dynamics and ecosystem processes, yet recognizing their evolutionary trends from satellite data remains challenging. We present a 3D U-Net-based multi-task framework that jointly performs ocean-front detection (OFD) and ocean-front evolutionary trend recognition (OFETR) from sea surface temperature [...] Read more.
Ocean fronts are central to upper-ocean dynamics and ecosystem processes, yet recognizing their evolutionary trends from satellite data remains challenging. We present a 3D U-Net-based multi-task framework that jointly performs ocean-front detection (OFD) and ocean-front evolutionary trend recognition (OFETR) from sea surface temperature gradient heatmaps. Instead of cascading OFD and OFETR in separate stages that pass OFD outputs downstream and can amplify upstream errors, the proposed model shares 3D spatiotemporal features and is trained end-to-end. We construct the Zhejiang–Fujian Coastal Front Mask (ZFCFM) and Evolutionary Trend (ZFCFET) datasets from ESA SST CCI L4 products for 2002–2021 and use them to evaluate the framework against 2D CNN baselines and traditional methods. Multi-task learning improves OFETR compared with single-task training while keeping OFD performance comparable, and the unified design reduces parameter count and daily computational cost. The model outputs daily point-level trend labels aligned with the dataset’s temporal resolution, indicating that end-to-end multi-task learning can mitigate error propagation and provide temporally resolved estimates. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition))
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28 pages, 65254 KB  
Article
SAM-Based Few-Shot Learning for Coastal Vegetation Segmentation in UAV Imagery via Cross-Matching and Self-Matching
by Yunfan Wei, Zhiyou Guo, Conghui Li, Weiran Li and Shengke Wang
Remote Sens. 2025, 17(20), 3404; https://doi.org/10.3390/rs17203404 - 10 Oct 2025
Viewed by 1387
Abstract
Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations [...] Read more.
Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-2i, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance. Full article
(This article belongs to the Special Issue Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience (Second Edition))
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