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Advances in Remote Sensing Image Target Detection and Recognition

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 12306

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Dr. Yin Zhuang

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National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP), Beijing Institute of Technology, Beijing 100081, China
Interests: representation learning; object detection; few-shot learning; semantic segmentation
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Special Issue Information

Dear Colleagues,

Remote sensing image target detection and recognition is a hot research topic in computer vison and can effectively extract valuable information from massive accessible remote sensing imagery data, supporting intelligent interpretation systems for earth observation. However, certain sophisticated challenges severely impact the performance of remote sensing image target detection and recognition, hindering intelligent interpretation algorithms’ application in practical systems. Specifically, in situations such as long-tail distribution, few-shot learning, domain shifts, real-time processing requirement, and so on, previously designed remote sensing image target detection and recognition algorithms deliver inferior performance. Thus, new mechanisms and methods need to be explored to improve the learning robustness, processing efficiency, and generalization ability of remote sensing image target detection and recognition, which will be crucial for establishing next-generation remote sensing intelligent interpretation systems.

This Special Issue aims to drive the development of target detection and recognition in the remote sensing domain, establishing a next-generation remote sensing detection and recognition algorithm. Topics may involve semi-supervised learning, transfer learning, and few-shot learning for remote sensing object detection or recognition, while considering specific challenges of remote sensing target characters, i.e., multi-scale, arbitrary orientation, tiny or weak objects, and so on.

Suggested themes and article types include the following:

Few-shot remote sensing object detection and recognition;
Zero-shot remote sensing object detection and recognition;
Cross-domain object detection and recognition in remote sensing domain;
Pretraining technology for remote sensing object detection and recognition;
Long-tail distribution object detection and recognition;
Open-vocabulary object detection in remote sensing domain.

Dr. Yin Zhuang
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 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-anonymized 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

semi-supervised learning
few-shot learning
long-tail distribution
domain adaptation
open-vocabulary
zero-shot learning

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Published Papers (11 papers)

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Research

29 pages, 10823 KB

Open AccessArticle

Marine Oil Film Segmentation Based on GCN-IGJO Method

by Jin Xu, Xingchen Luo, Zhaobin Fu, Mengxin Sun, Minghao Yan, Zekun Guo, Binghui Chen, Gaorui Tu, Bingxin Liu, Haihui Dong and Siow Chee Loon

Remote Sens. 2026, 18(16), 2660; https://doi.org/10.3390/rs18162660 - 7 Aug 2026

Viewed by 290

Abstract

Marine oil spills pose significant threats to ecosystems and coastal economies, making accurate oil film detection from remote sensing data a critical task. This study proposes a two-stage method, GCN-IGJO, for segmenting oil films in challenging X-band radar images. The method first uses a Graph Convolutional Network (GCN) to learn high-order features from pixel data, enabling effective initial region extraction. Subsequently, an Improved Golden Jackal Optimization (IGJO) algorithm is introduced to perform precise threshold segmentation, incorporating specialized strategies to bias the search towards the low-intensity values characteristic of oil slicks. Experimental comparisons demonstrate that the proposed GCN-IGJO method achieves a superior balance between high precision and good recall, outperforming several baseline and alternative methods. The results validate the effectiveness of combining deep graph learning with an enhanced metaheuristic optimizer for the accurate segmentation of weak-target oil films. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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22 pages, 68851 KB

Open AccessArticle

The Topological Detection of Spatially Proximate Emitters in Spaceborne-Radio-Environment Maps: An ImprovedPersistent-Homology Approach

by Ziyi Zhang, Shunhu Hou, Youchen Fan and Shengliang Fang

Remote Sens. 2026, 18(13), 2105; https://doi.org/10.3390/rs18132105 - 29 Jun 2026

Viewed by 456

Abstract

Existing radio environment map(REM)-based emitter-detection methods suffer from high false positives and missed detections in blurred or conjoined structures, or require large annotated datasets and heavy computation. We propose an unsupervised method, persistent homology with agglomerative clustering (PH-AC), based on an improved persistent-homology algorithm. A simulated spaceborne-REM dataset is constructed via synthetic-aperture passive interferometric imaging, covering isolated, adjacent-pair, and complex-emitter distributions. Persistent homology tracks the birth, death, and merging of zero-dimensional connected components as the intensity threshold varies. To address missed detections for spatially proximate emitters, multidimensional topological features are constructed via feature-contribution analysis. Agglomerative clustering with Ward linkage then adaptively separates emitters from noise without supervision. Experimental results show that PH-AC achieves a perfect F1 score of 1.000 in isolated scenarios; for adjacent emitters, it improves F1 by 15.7% over the best image-processing method and stays within 4% of supervised deep learning methods, while requiring no annotations. In complex environments, it attains an F1 of 0.937, outperforming all compared methods. Its computational complexity is only

2.25 \times 10^{6}

FLOPs, three orders lower than YOLO-based detectors. This work offers a lightweight, annotation-free topological paradigm for spaceborne-REM-emitter detection. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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24 pages, 7417 KB

Open AccessArticle

MSFE-Net: A Task-Oriented Optical–SAR Fusion Framework for Robust Industrial Object Detection

by Rufeng Guo, Rong Gui, Jun Hu, Pinjun Tang, Liang Cao, Jinghui Zhang and Qiao Jiang

Remote Sens. 2026, 18(10), 1466; https://doi.org/10.3390/rs18101466 - 8 May 2026

Cited by 1 | Viewed by 638

Abstract

Object detection in high-resolution remote sensing images under complex industrial environments is fundamentally constrained by the inherent limitations of single-modality sensors. Optical imagery is prone to background confusion and pseudo-target interference, while synthetic aperture radar (SAR) imagery suffers from speckle noise and structural ambiguity. This work investigates a critical evaluation gap in multimodal fusion, where traditional image-level quality metrics do not consistently reflect downstream detection performance. To address this issue, we propose a task-oriented framework termed the Multi-Source Fusion for Enhanced Object Detection Network (MSFE-Net). The proposed method integrates pixel-level optical–SAR fusion with a YOLOv11-based detector, enabling the learning of task-relevant representations by exploiting complementary optical spectral cues and SAR scattering characteristics. Extensive experiments are conducted across multiple fusion strategies and representative detection architectures on two industrial datasets covering oil tanks and photovoltaic arrays. The results consistently reveal a nonlinear decoupling between image-level fusion metrics and detection accuracy, indicating that improvements in global statistical image quality do not necessarily lead to superior task performance. Furthermore, the proposed framework demonstrates improved robustness in complex scenarios involving multi-scale and weak targets. Specifically, MSFE-Net achieves 99.1% mAP@50 for oil tank detection (19.5% improvement over SAR-only baselines) and 90.2% mAP@50 for photovoltaic array detection, with stable performance across different evaluation settings. These results highlight the importance of task-oriented evaluation in multimodal remote sensing fusion and suggest that downstream detection performance provides a more reliable criterion than conventional image-quality metrics. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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22 pages, 5913 KB

Open AccessArticle

Towards Balanced Supervision: Cumulative Quality-Based Dynamic Assignment for Fine-Grained Remote Sensing Object Detection

by Yida Pan, Haoran Zhu, Zijuan Chen, Guangyou Yang and Wen Yang

Remote Sens. 2026, 18(9), 1406; https://doi.org/10.3390/rs18091406 - 2 May 2026

Viewed by 559

Abstract

Fine-grained object detection (FGOD) is crucial for identifying visually similar sub-categories in remote sensing imagery. However, existing detectors suffer from severe supervision imbalance because static label assignment strategies assign a fixed number of positive samples to all sub-categories and targets. To address this challenge, this paper presents Cumulative Quality-based Dynamic Assignment (CQDA), a fine-grained aware label assignment algorithm that dynamically calculates the optimal positive budget for each instance based on its cumulative alignment quality. Moreover, to further resolve feature-space confusion, this paper introduces two modules: a frequency-decoupled enhancement algorithm to sharpen discriminative features, and an orthogonal classification head to maximize inter-class separability. Integrated into the KFIoU framework, extensive experiments demonstrate that the proposed method consistently achieves performance improvements of 4.2, 15.8, and 35.3 in mAP@0.5 on the fine-grained oriented object detection datasets FAIR1M-v2, MAR20, and ShipRSImageNet, respectively. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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27 pages, 3724 KB

Open AccessArticle

A Deployment-Oriented Real-Time Transformer Detector and Benchmark for Maritime Search and Rescue Under Severe Sea Clutter

by Zhonghao Wang, Xin Liu, Wenlong Sun, Qixiang Liu, Yijie Cai and Yong Hu

Remote Sens. 2026, 18(8), 1258; https://doi.org/10.3390/rs18081258 - 21 Apr 2026

Viewed by 518

Abstract

Maritime search and rescue (SAR) is a time-critical public safety mission that increasingly relies on unmanned vehicles to localize persons overboard. However, reliable onboard perception is challenged by extreme scale variation and heavy sea clutter under strict latency and compute budgets. We present R-DET, a deployment-oriented end-to-end Transformer detector built on the RT-DETR paradigm, featuring three rescue-oriented designs: (i) a lightweight backbone (Rescue-Net) preserving multi-scale cues, (ii) a bounded-cost global-context module (Rescue Attention) suppressing sea clutter, and (iii) an efficient fusion module (Rescue-FPN) injecting high-resolution details for tiny targets. We further introduce MarineRescue-8K, a benchmark collected from real maritime operations with a mission-aligned ignore region protocol that reduces the influence of non-critical clutter during optimization and evaluation. On MarineRescue-8K, R-DET achieves 84.1% mAP@0.5 with only 14.5 M parameters at 63.2 FPS (RTX 2080 SUPER), demonstrating a favorable accuracy–efficiency trade-off for deployment-oriented maritime SAR perception. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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25 pages, 11348 KB

Open AccessArticle

Uncertainty-Aware Cross-Domain Few-Shot Scene Classification from Remote Sensing Imagery

by Zifan Ning, Can Li, He Chen, Guangyao Zhou, Shanghang Zhang, Lianlin Li and Yin Zhuang

Remote Sens. 2026, 18(8), 1233; https://doi.org/10.3390/rs18081233 - 18 Apr 2026

Viewed by 615

Abstract

Cross-Domain Few-Shot Scene Classification (CDFSSC) aims to transfer knowledge from a source domain to a target domain for few-shot classification tasks, and is essential for remote sensing applications involving diverse platforms and dynamic environments. However, distribution discrepancies and category misalignment across domains often introduce high predictive uncertainty, significantly degrading model performance. To address these challenges, an uncertainty-aware cross-domain (UACD) framework is proposed to enhance model reliability by systematically mining uncertainty-related information. Specifically, in the cross-domain training process, a feature-decision consistency regularization (FDCR) structure is designed to stabilize cross-domain training by enforcing consistency at both feature and decision levels. Furthermore, an uncertainty-aware knowledge mining (UKM) policy is introduced to effectively exploit high-uncertainty target samples, mitigating the negative impact of unreliable pseudo-labels and improving representation learning. In the few-shot adaptation stage, an uncertainty-aware predictor is developed to enhance adaptability and decision-making in target tasks. Extensive experiments on 12 cross-domain scenarios demonstrate that the proposed UACD framework consistently achieves superior or competitive performance, with strong robustness and generalization capability across diverse CDFSSC tasks. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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30 pages, 11698 KB

Open AccessArticle

RShDet: An Adaptive Spectral-Aware Network for Remote Sensing Object Detection Under Haze Corruption

by Wei Zhang, Yuantao Wang, Haowei Yang and Xuerui Mao

Remote Sens. 2026, 18(7), 1020; https://doi.org/10.3390/rs18071020 - 29 Mar 2026

Cited by 1 | Viewed by 774

Abstract

Remote sensing (RS) object detection faces intrinsic challenges arising from the overhead imaging paradigm and the diversity of climatic conditions. In particular, atmospheric phenomena such as clouds and haze cause severe visual degradation, making reliable object detection difficult. However, most existing detectors are developed under clear-weather conditions, which limits their generalization capability in realistic haze-degraded RS scenarios. To alleviate this issue, an adaptive spectral-aware network for RS object detection under haze interference is proposed, termed RShDet, which is designed to handle both high-altitude RS imagery and low-altitude Unmanned Aerial Vehicle (UAV) scenarios. Firstly, the Object-Centered Dynamic Enhancement (OCDE) module dynamically adjusts the spatial positions of key-value pairs through query-agnostic offsets, enabling the network to emphasize object-relevant regions while suppressing haze-induced background interference. Secondly, the Dynamic Multi-Spectral Perception and Filtering (DSPF) module introduces a multi-spectral attention mechanism that adaptively selects informative frequency components, thereby enhancing discriminative feature representations in hazy environments. Thirdly, the Frequency-Domain Multi-Feature Fusion (FDMF) module employs learnable weights to complementarily integrate amplitude and phase information in the frequency domain, enabling effective cross-task feature interaction between the enhancement and detection branches. Extensive experiments demonstrate that RShDet consistently achieves superior detection performance under hazy conditions across both synthetic and real-world benchmarks. Specifically, it achieves improvements of 2.4% mAP50 on Hazy-DOTA, 1.9% mAP on HazyDet, and 2.33% mAP on the real-world foggy dataset RTTS, surpassing existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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24 pages, 4385 KB

Open AccessArticle

HTMNet: Hybrid Transformer–Mamba Network for Hyperspectral Target Detection

by Xiaosong Zheng, Yin Kuang, Yu Huo, Wenbo Zhu, Min Zhang and Hai Wang

Remote Sens. 2025, 17(17), 3015; https://doi.org/10.3390/rs17173015 - 30 Aug 2025

Cited by 5 | Viewed by 2043

Abstract

Hyperspectral target detection (HTD) aims to identify pixel-level targets within complex backgrounds, but existing HTD methods often fail to fully exploit multi-scale features and integrate global–local information, leading to suboptimal detection performance. To address these challenges, a novel hybrid Transformer–Mamba network (HTMNet) is proposed to reconstruct the high-fidelity background samples for HTD. HTMNet consists of the following two parallel modules: the multi-scale feature extraction (MSFE) module and the global–local feature extraction (GLFE) module. Specifically, in the MSFE module, we designed a multi-scale Transformer to extract and fuse multi-scale background features. In the GLFE module, a global feature extraction (GFE) module is devised to extract global background features by introducing a spectral–spatial attention module in the Transformer. Meanwhile, a local feature extraction (LFE) module is developed to capture local background features by incorporating the designed circular scanning strategy into the LocalMamba. Additionally, a feature interaction fusion (FIF) module is devised to integrate features from multiple perspectives, enhancing the model’s overall representation capability. Experiments show that our method achieves AUC(P_F, P_D) scores of 99.97%, 99.91%, 99.82%, and 99.64% on four public hyperspectral datasets. These results demonstrate that HTMNet consistently surpasses state-of-the-art HTD methods, delivering superior detection performance in terms of AUC(P_F, P_D). Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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23 pages, 16581 KB

Open AccessArticle

SLD-YOLO: A Lightweight Satellite Component Detection Algorithm Based on Multi-Scale Feature Fusion and Attention Mechanism

by Yonghao Li, Hang Yang, Bo Lü and Xiaotian Wu

Remote Sens. 2025, 17(17), 2950; https://doi.org/10.3390/rs17172950 - 25 Aug 2025

Cited by 1 | Viewed by 1780

Abstract

Space-based on-orbit servicing missions impose stringent requirements for precise identification and localization of satellite components, while existing detection algorithms face dual challenges of insufficient accuracy and excessive computational resource consumption. This paper proposes SLD-YOLO, a lightweight satellite component detection model based on improved YOLO11, balancing accuracy and efficiency through structural optimization and lightweight design. First, we design RLNet, a lightweight backbone network that employs reparameterization mechanisms and hierarchical feature fusion strategies to reduce model complexity by 19.72% while maintaining detection accuracy. Second, we propose the CSP-HSF multi-scale feature fusion module, used in conjunction with PSConv downsampling, to effectively improve the model’s perception of multi-scale objects. Finally, we introduce SimAM, a parameter-free attention mechanism in the detection head to further improve feature representation capability. Experiments on the UESD dataset demonstrate that SLD-YOLO achieves measurable improvements compared to the baseline YOLO11s model across five satellite component detection categories: mAP50 increases by 2.22% to 87.44%, mAP50:95 improves by 1.72% to 63.25%, while computational complexity decreases by 19.72%, parameter count reduces by 25.93%, model file size compresses by 24.59%, and inference speed reaches 90.4 FPS. Validation experiments on the UESD_edition2 dataset further confirm the model’s robustness. This research provides an effective solution for target detection tasks in resource-constrained space environments, demonstrating practical engineering application value. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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22 pages, 5535 KB

Open AccessArticle

OFNet: Integrating Deep Optical Flow and Bi-Domain Attention for Enhanced Change Detection

by Liwen Zhang, Quan Zou, Guoqing Li, Wenyang Yu, Yong Yang and Heng Zhang

Remote Sens. 2025, 17(17), 2949; https://doi.org/10.3390/rs17172949 - 25 Aug 2025

Cited by 3 | Viewed by 1926

Abstract

Change detection technology holds significant importance in disciplines such as urban planning, land utilization tracking, and hazard evaluation, as it can efficiently and accurately reveal dynamic regional change processes, providing crucial support for scientific decision-making and refined management. Although deep learning methods based on computer vision have achieved remarkable progress in change detection, they still face challenges including reducing dynamic background interference, capturing subtle changes, and effectively fusing multi-temporal data features. To address these issues, this paper proposes a novel change detection model called OFNet. Building upon existing Siamese network architectures, we introduce an optical flow branch module that supplements pixel-level dynamic information. By incorporating motion features to guide the network’s attention to potential change regions, we enhance the model’s ability to characterize and discriminate genuine changes in cross-temporal remote sensing images. Additionally, we innovatively propose a dual-domain attention mechanism that simultaneously models discriminative features in both spatial and frequency domains for change detection tasks. The spatial attention focuses on capturing edge and structural changes, while the frequency-domain attention strengthens responses to key frequency components. The synergistic fusion of these two attention mechanisms effectively improves the model’s sensitivity to detailed changes and enhances the overall robustness of detection. Experimental results demonstrate that OFNet achieves an IoU of 83.03 on the LEVIR-CD dataset and 82.86 on the WHU-CD dataset, outperforming current mainstream approaches and validating its superior detection performance and generalization capability. This presents a novel technical method for environmental observation and urban transformation analysis tasks. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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26 pages, 6806 KB

Open AccessArticle

Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy

by Zhaohong Li, Wei Yang, Can Su, Hongcheng Zeng, Yamin Wang, Jiayi Guo and Huaping Xu

Remote Sens. 2025, 17(15), 2599; https://doi.org/10.3390/rs17152599 - 26 Jul 2025

Viewed by 1382

Abstract

The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Image Target Detection and Recognition)

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