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Remote Sensing
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Remote Sensing of Target Object Detection and Identification (Third Edition)
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Remote Sensing of Target Object Detection and Identification (Third Edition)

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 December 2025 | Viewed by 3060

Special Issue Editor

Dr. Paolo Tripicchio

E-Mail Website
Guest Editor
Scuola Superiore Sant’Anna di Studi Universitari e di Perfezionamento, Pisa, Italy
Interests: artificial intelligence; computer vision; 3D reconstruction; image processing; localization methods; mapping; inspection robotics; deep Learning; industrial monitoring; smart sensors; photogrammetry; LiDAR; SAR; farming applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching the third Special Issue of Remote Sensing to be released under the title “Remote Sensing of Target Object Detection and Identification”.

The ability to detect and identify target objects from remote images and acquisitions is paramount in remote sensing systems to enable a proper analysis of the territories. The field of the appliance of such a technology spans environmental and urban monitoring, hazard and disaster management, and defense and military applications. The existing literature has taken advantage of the large amounts of data acquired by sensors mounted on satellite, airborne, and unmanned aerial vehicle (UAV) platforms. Such works exploit different phenomena and technologies that include synthetic aperture radar (SAR) imaging, multispectral and hyperspectral imaging, and images (or videos) acquired in the visible and near-infrared (VNIR) wavelength range. With recent improvements in sensing technologies regarding their spatial resolution and spectral content, and with the rapid development of artificial intelligence techniques that exploit convolutional neural networks (CNNs) or deep neural networks (DNNs), the results that novel approaches will achieve in the near future are promising.

This Special Issue aims to collect contributions in the abovementioned fields, including papers on theoretical/methodological and technological applications. It invites topics associated with (but not limited to) the following areas:

  • Target object detection and identification for urban and infrastructure monitoring;
  • Target object detection and identification for agricultural monitoring;
  • Target object detection, tracking, and identification for security and military applications;
  • Target object detection, tracking, and identification for the environmental monitoring of the sea;
  • Optical, infrared, and multispectral datasets for target object detection and identification;
  • Multi-sensor (spatio-temporal, spatio-spectral, and multimodal) data fusion for target object detection and identification;
  • Methods, algorithms, and theoretical models for target object detection, tracking, and identification;
  • Machine learning and deep learning approaches for target object detection, tracking, and identification.

This Special Issue also welcomes novel developments regarding sensing elements and apparatus to remotely detect, track and identify objects.

Dr. Paolo Tripicchio
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

  • remote sensing
  • data fusion
  • machine learning
  • deep learning
  • signal processing
  • classification
  • target tracking

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Related Special Issue

Published Papers (5 papers)

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Research

29 pages, 9314 KiB  
Article
SFRADNet: Object Detection Network with Angle Fine-Tuning Under Feature Matching
by Keliang Liu, Yantao Xi, Donglin Jing, Xue Zhang and Mingfei Xu
Remote Sens. 2025, 17(9), 1622; https://doi.org/10.3390/rs17091622 - 2 May 2025
Viewed by 250
Abstract
Due to the distant acquisition and bird’s-eye perspective of remote sensing images, ground objects are distributed in arbitrary scales and multiple orientations. Existing detectors often utilize feature pyramid networks (FPN) and deformable (or rotated) convolutions to adapt to variations in object scale and [...] Read more.
Due to the distant acquisition and bird’s-eye perspective of remote sensing images, ground objects are distributed in arbitrary scales and multiple orientations. Existing detectors often utilize feature pyramid networks (FPN) and deformable (or rotated) convolutions to adapt to variations in object scale and orientation. However, these methods solve scale and orientation issues separately and ignore their deeper coupling relationships. When the scale features extracted by the network are significantly mismatched with the object, it is difficult for the detection head to effectively capture orientation of object, resulting in misalignment between object and bounding box. Therefore, we propose a one-stage detector—Scale First Refinement-Angle Detection Network (SFRADNet), which aims to fine-tune the rotation angle under precise scale feature matching. We introduce the Group Learning Large Kernel Network (GL2KNet) as the backbone of SFRADNet and employ a Shape-Aware Spatial Feature Extraction Module (SA-SFEM) as the primary component of the detection head. Specifically, within GL2KNet, we construct diverse receptive fields with varying dilation rates to capture features across different spatial coverage ranges. Building on this, we utilize multi-scale features within the layers and apply weighted aggregation based on a Scale Selection Matrix (SSMatrix). The SSMatrix dynamically adjusts the receptive field coverage according to the target size, enabling more refined selection of scale features. Based on precise scale features captured, we first design a Directed Guiding Box (DGBox) within the SA-SFEM, using its shape and position information to supervise the sampling points of the convolution kernels, thereby fitting them to deformations of object. This facilitates the extraction of orientation features near the object region, allowing for accurate refinement of both scale and orientation. Experiments show that our network achieves a mAP of 80.10% on the DOTA-v1.0 dataset, while reducing computational complexity compared to the baseline model. Full article
(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification (Third Edition))
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25 pages, 18221 KiB  
Article
Self-Supervised Feature Contrastive Learning for Small Weak Object Detection in Remote Sensing
by Zheng Li, Xueyan Hu, Jin Qian, Tianqi Zhao, Dongdong Xu and Yongcheng Wang
Remote Sens. 2025, 17(8), 1438; https://doi.org/10.3390/rs17081438 - 17 Apr 2025
Viewed by 318
Abstract
Despite advances in remote sensing object detection, accurately identifying small, weak objects remains challenging. Their limited pixel representation often fails to capture distinctive features, making them susceptible to environmental interference. Current detectors frequently miss these subtle feature variations. To address these challenges, we [...] Read more.
Despite advances in remote sensing object detection, accurately identifying small, weak objects remains challenging. Their limited pixel representation often fails to capture distinctive features, making them susceptible to environmental interference. Current detectors frequently miss these subtle feature variations. To address these challenges, we propose FCDet, a feature contrast-based detector for small, weak objects. Our approach introduces: (1) a spatial-guided feature upsampler (SGFU) that aligns features by adaptive sampling based on spatial distribution, thus achieving fine-grained alignment during feature aggregation; (2) a feature contrast head (FCH) that projects GT and RoI features into an embedding space for discriminative learning; and (3) an instance-controlled label assignment (ICLA) strategy that optimizes sample selection for feature contrastive learning. We conduct comprehensive experiments on challenging datasets, with the proposed method achieving 73.89% mAP on DIOR, 95.04% mAP on NWPU VHR-10, and 26.4% AP on AI-TOD, demonstrating its effectiveness and superior performance. Full article
(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification (Third Edition))
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27 pages, 19695 KiB  
Article
Low-Shot Weakly Supervised Object Detection for Remote Sensing Images via Part Domination-Based Active Learning and Enhanced Fine-Tuning
by Peng Liu, Boxue Huang, Tingting Jin and Hui Long
Remote Sens. 2025, 17(7), 1155; https://doi.org/10.3390/rs17071155 - 25 Mar 2025
Viewed by 390
Abstract
In low-shot weakly supervised object detection (LS-WSOD), a small number of strong (instance-level) labels are introduced to a weakly (image-level) annotated dataset, thus balancing annotation costs and model performance. To address issues in LS-WSOD in remote sensing images (RSIs) such as part domination, [...] Read more.
In low-shot weakly supervised object detection (LS-WSOD), a small number of strong (instance-level) labels are introduced to a weakly (image-level) annotated dataset, thus balancing annotation costs and model performance. To address issues in LS-WSOD in remote sensing images (RSIs) such as part domination, context confusion, class imbalance, and noise, we propose a novel active learning strategy and an enhanced fine-tuning mechanism. Specifically, we designed a part domination-based adaptive active learning (PDAAL) strategy to discover the most informative and challenging samples for instance-level annotation. PDAAL also applies an adaptive threshold to balance sampling frequencies for long-tailed class distributions. For enhanced fine-tuning, we first developed a parameter-efficient attention for context (PAC) module that learns spatial attention relationships, mitigating context confusion and accelerating the convergence of fine-tuning. Furthermore, we present an adaptive category resampling for tuning (ACRT) mechanism for resampling strong annotation data. ACRT contributes to refining the model at different active stages, especially for under-performed classes, and to reducing the impact of noisy predictions. Experimental results on the NWPU VHR-10.v2 and DIOR datasets show that our method outperforms state-of-the-art LS-WSOD baselines by 4.5% and 3.1% in mAP, respectively, demonstrating that our framework offers an efficient solution for LS-WSOD in RSIs. Full article
(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification (Third Edition))
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28 pages, 12000 KiB  
Article
On-Satellite Implementation of Real-Time Multi-Object Moving Vehicle Tracking with Complex Moving Backgrounds
by Jingyi Yu, Siyuan Wei, Yuxiao Wen, Danshu Zhou, Runjiang Dou, Xiuyu Wang, Jiangtao Xu, Jian Liu, Nanjian Wu and Liyuan Liu
Remote Sens. 2025, 17(3), 418; https://doi.org/10.3390/rs17030418 - 26 Jan 2025
Viewed by 680
Abstract
On-satellite information processing enables all-weather target tracking. The background of videos from satellite sensors exhibits an affine transformation due to their motion relative to the Earth. In complex moving backgrounds, moving vehicles have a small number of pixels and weak texture features. At [...] Read more.
On-satellite information processing enables all-weather target tracking. The background of videos from satellite sensors exhibits an affine transformation due to their motion relative to the Earth. In complex moving backgrounds, moving vehicles have a small number of pixels and weak texture features. At the same time, the resources and performance of on-satellite equipment are limited. To address these issues, we propose a multi-object tracking (MOT) algorithm with a detection–association framework for moving vehicles in complex moving backgrounds and implement the algorithm on a satellite to achieve real-time MOT. We use feature matching to effectively eliminate the effects of background motion and use the neighborhood pixel difference method to extract moving vehicle targets in the detection stage. The accurate extraction of motion targets ensures the effectiveness of target association to achieve MOT of moving vehicles in complex moving backgrounds. Additionally, we use a Field-Programmable Gate Array (FPGA) to implement the algorithm completely on a satellite. We propose a pixel-level stream processing mode and a cache access processing mode, given the characteristics of on-satellite equipment and sensors. According to the experimental results, the prototype on-satellite implementation method proposed in this paper can achieve real-time processing at 1024 × 1024 px@47 fps. Full article
(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification (Third Edition))
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20 pages, 1587 KiB  
Article
Infrared Dim Small Target Detection Algorithm with Large-Size Receptive Fields
by Xiaozhen Wang, Chengshan Han, Jiaqi Li, Ting Nie, Mingxuan Li, Xiaofeng Wang and Liang Huang
Remote Sens. 2025, 17(2), 307; https://doi.org/10.3390/rs17020307 - 16 Jan 2025
Viewed by 864
Abstract
Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. [...] Read more.
Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics. Full article
(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification (Third Edition))
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