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Remote Sensing of Target Object Detection and Identification II

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 10916

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Special Issue Editor

Dr. Paolo Tripicchio

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Guest Editor

Gustavo Stefanini Advanced Robotics Research Center, Scuola Superiore Sant’Anna di Studi Universitari e di Perfezionamento, Pisa, Italy
Interests: robotics; artificial intelligence; computer vision; 3D reconstruction; image processing; localization methods; mapping; inspection robotics; deep learning; industrial monitoring; smart sensors; photogrammetry; LiDAR; SAR; farming applications
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Special Issue Information

Dear Colleagues,

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 the 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 the recent improvements in the 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 is promising.

This Special Issue aims at collecting contributions in the above-mentioned fields, including both theoretical/methodological and technological applications papers. 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

Published Papers (11 papers)

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Research

Jump to: Review

21 pages, 6695 KiB

Open AccessArticle

MVT: Multi-Vision Transformer for Event-Based Small Target Detection

by Shilong Jing, Hengyi Lv, Yuchen Zhao, Hailong Liu and Ming Sun

Remote Sens. 2024, 16(9), 1641; https://doi.org/10.3390/rs16091641 - 4 May 2024

Viewed by 565

Abstract

Object detection in remote sensing plays a crucial role in various ground identification tasks. However, due to the limited feature information contained within small targets, which are more susceptible to being buried by complex backgrounds, especially in extreme environments (e.g., low-light, motion-blur scenes). Meanwhile, event cameras offer a unique paradigm with high temporal resolution and wide dynamic range for object detection. These advantages enable event cameras without being limited by the intensity of light, to perform better in challenging conditions compared to traditional cameras. In this work, we introduce the Multi-Vision Transformer (MVT), which comprises three efficiently designed components: the downsampling module, the Channel Spatial Attention (CSA) module, and the Global Spatial Attention (GSA) module. This architecture simultaneously considers short-term and long-term dependencies in semantic information, resulting in improved performance for small object detection. Additionally, we propose Cross Deformable Attention (CDA), which progressively fuses high-level and low-level features instead of considering all scales at each layer, thereby reducing the computational complexity of multi-scale features. Nevertheless, due to the scarcity of event camera remote sensing datasets, we provide the Event Object Detection (EOD) dataset, which is the first dataset that includes various extreme scenarios specifically introduced for remote sensing using event cameras. Moreover, we conducted experiments on the EOD dataset and two typical unmanned aerial vehicle remote sensing datasets (VisDrone2019 and UAVDT Dataset). The comprehensive results demonstrate that the proposed MVT-Net achieves a promising and competitive performance. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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19 pages, 7455 KiB

Open AccessArticle

Scattering-Point-Guided Oriented RepPoints for Ship Detection

by Weishan Zhao, Lijia Huang, Haitian Liu and Chaobao Yan

Remote Sens. 2024, 16(6), 933; https://doi.org/10.3390/rs16060933 - 7 Mar 2024

Viewed by 674

Abstract

Ship detection finds extensive applications in fisheries management, maritime rescue, and surveillance. However, detecting nearshore targets in SAR images is challenging due to land scattering interference and non-axisymmetric ship shapes. Existing SAR ship detection models struggle to adapt to oriented ship detection in complex nearshore environments. To address this, we propose an oriented-reppoints target detection scheme guided by scattering points in SAR images. Our method deeply integrates SAR image target scattering characteristics and designs an adaptive sample selection scheme guided by target scattering points. This incorporates scattering position features into the sample quality measurement scheme, providing the network with a higher-quality set of proposed reppoints. We also introduce a novel supervised guidance paradigm that uses target scattering points to guide the initialization of reppoints, mitigating the influence of land scattering interference on the initial reppoints quality. This achieves adaptive feature learning, enhancing the quality of the initial reppoints set and the performance of object detection. Our method has been extensively tested on the SSDD and HRSID datasets, where we achieved mAP scores of 89.8% and 80.8%, respectively. These scores represent significant improvements over the baseline methods, demonstrating the effectiveness and robustness of our approach. Additionally, our method exhibits strong anti-interference capabilities in nearshore detection and has achieved state-of-the-art performance. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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20 pages, 1830 KiB

Open AccessArticle

Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection

by Xiaozhen Wang, Chengshan Han, Jiaqi Li, Ting Nie, Mingxuan Li, Xiaofeng Wang and Liang Huang

Remote Sens. 2024, 16(4), 643; https://doi.org/10.3390/rs16040643 - 9 Feb 2024

Viewed by 939

Abstract

The technology of infrared dim- and small-target detection is irreplaceable in many fields, such as those of missile early warning systems and forest fire prevention, among others. However, numerous components interfere with infrared imaging, presenting challenges for achieving successful detection of infrared dim and small targets with a low rate of false alarms. Hence, we propose a new infrared dim- and small-target detection network, Multiscale Feature Extraction U-Net for Infrared Dim- and Small-Target Detection (MFEU-Net), which can accurately detect targets in complex backgrounds. It uses the U-Net structure, and the encoders and decoders consist of ReSidual U-block and Inception, allowing rich multiscale feature information to be extracted. Thus, the effectiveness of algorithms in detecting very small-sized targets can be improved. In addition, through the multidimensional channel and spatial attention mechanism, the model can be adjusted to focus more on the target area in the image, improving its extraction of target information and detection performance in different scenarios. The experimental results show that our proposed algorithm outperforms other advanced algorithms in detection performance. On the MFIRST, SIRST, and IRSTD-1k datasets, we achieved detection rates of 0.864, 0.962, and 0.965; IoU values of 0.514, 0.671, and 0.630; and false alarm rates of 3.08 ×

10^{- 5}

, 2.61 ×

10^{- 6}

, and 1.81 ×

10^{- 5}

, respectively. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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Graphical abstract

17 pages, 5281 KiB

Open AccessArticle

Tracking-by-Detection Algorithm for Underwater Target Based on Improved Multi-Kernel Correlation Filter

by Wenrong Yue, Feng Xu and Juan Yang

Remote Sens. 2024, 16(2), 323; https://doi.org/10.3390/rs16020323 - 12 Jan 2024

Viewed by 699

Abstract

Joint detection and tracking of weak underwater targets are challenging problems whose complexity is intensified when the target is disturbed by reverberation. In the low signal-to-reverberation ratio (SRR) environment, the traditional detection and tracking methods perform poorly in tracking robustness because they only consider the target motion characteristics. Recently, the kernel correlation filter (KCF) based on target features has received lots of attention and gained great success in visual tracking. We propose an improved multi-kernel correlation filter (IMKCF) tracking-by-detection algorithm by introducing the KCF into the field of underwater weak target detection and tracking. It is composed of the tracking-by-detection, the adaptive reliability check, and the re-detection modules. Specifically, the tracking-by-detection part is built on the multi-kernel correlation filter (MKCF), and it uses multi-frame data weighted averaging to update. The reliability check helps keep the tracker from corruption. The re-detection module, integrated with a Kalman filter, identifies target positions when the tracking is unreliable. Finally, the experimental data processing and analysis show that the proposed method outperforms the single-kernel methods and some traditional tracking methods. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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19 pages, 10955 KiB

Open AccessArticle

Multi-Dimensional Low-Rank with Weighted Schatten p-Norm Minimization for Hyperspectral Anomaly Detection

by Xi’ai Chen, Zhen Wang, Kaidong Wang, Huidi Jia, Zhi Han and Yandong Tang

Remote Sens. 2024, 16(1), 74; https://doi.org/10.3390/rs16010074 - 24 Dec 2023

Viewed by 615

Abstract

Hyperspectral anomaly detection is an important unsupervised binary classification problem that aims to effectively distinguish between background and anomalies in hyperspectral images (HSIs). In recent years, methods based on low-rank tensor representations have been proposed to decompose HSIs into low-rank background and sparse anomaly tensors. However, current methods neglect the low-rank information in the spatial dimension and rely heavily on the background information contained in the dictionary. Furthermore, these algorithms show limited robustness when the dictionary information is missing or corrupted by high level noise. To address these problems, we propose a novel method called multi-dimensional low-rank (MDLR) for HSI anomaly detection. It first reconstructs three background tensors separately from three directional slices of the background tensor. Then, weighted schatten p-norm minimization is employed to enforce the low-rank constraint on the background tensor, and

L_{F, 1}

-norm regularization is used to describe the sparsity in the anomaly tensor. Finally, a well-designed alternating direction method of multipliers (ADMM) is employed to effectively solve the optimization problem. Extensive experiments on four real-world datasets show that our approach outperforms existing anomaly detection methods in terms of accuracy. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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21 pages, 2659 KiB

Open AccessArticle

Infrared Small Dim Target Detection Using Group Regularized Principle Component Pursuit

by Meihui Li, Yuxing Wei, Bingbing Dan, Dongxu Liu and Jianlin Zhang

Remote Sens. 2024, 16(1), 16; https://doi.org/10.3390/rs16010016 - 20 Dec 2023

Cited by 1 | Viewed by 714

Abstract

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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23 pages, 6112 KiB

Open AccessArticle

Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

by Xuying Hao, Xianyuan Liu, Yujia Liu, Yi Cui and Tao Lei

Remote Sens. 2023, 15(22), 5424; https://doi.org/10.3390/rs15225424 - 20 Nov 2023

Cited by 1 | Viewed by 943

Abstract

Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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26 pages, 5470 KiB

Open AccessArticle

LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

by Yang Tian, Xuan Wang, Shengjie Zhu, Fang Xu and Jinghong Liu

Remote Sens. 2023, 15(17), 4358; https://doi.org/10.3390/rs15174358 - 4 Sep 2023

Cited by 4 | Viewed by 1103

Abstract

Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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18 pages, 24423 KiB

Open AccessArticle

Infrared Dim Star Background Suppression Method Based on Recursive Moving Target Indication

by Lei Zhang, Peng Rao, Yang Hong, Xin Chen and Liangjie Jia

Remote Sens. 2023, 15(17), 4152; https://doi.org/10.3390/rs15174152 - 24 Aug 2023

Cited by 1 | Viewed by 881

Abstract

Space-based infrared target detection can provide full-time and full-weather observation of targets, thus it is of significance in space security. However, the presence of stars in the background can severely affect the accuracy and real-time performance of infrared dim and small target detection, making star suppression a key technology and hot spot in the field of space target detection. The existing star suppression algorithms are all oriented towards the detection before track method and rely on the single image properties of the stars. They can only effectively suppress bright stars with a high signal-to-noise ratio (SNR). To address this problem, we propose a new method for infrared dim star background suppression based on recursive moving target indication (RMTI). Our proposed method is based on a more direct analysis of the image sequence itself, which will lead to more robust and accurate background suppression. The method first obtains the motion information of stars through satellite motion or key star registration. Then, the advanced RMTI algorithm is used to enhance the stars in the image. Finally, the mask of suppressing stars is generated by an accumulation frame adaptive threshold. The experimental results show that the algorithm has a less than 8.73% leakage suppression rate for stars with an SNR ≤ 2 and a false suppression rate of less than 2.3%. The validity of the proposed method is verified in real data. Compared with the existing methods, the method proposed in this paper can stably suppress stars with a lower SNR. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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34 pages, 9770 KiB

Open AccessArticle

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

by Yueqi Su, Xin Chen, Gaorui Liu, Chen Cang and Peng Rao

Remote Sens. 2023, 15(12), 3156; https://doi.org/10.3390/rs15123156 - 16 Jun 2023

Cited by 1 | Viewed by 1899

Abstract

Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10⁻⁴. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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Review

Jump to: Research

41 pages, 22605 KiB

Open AccessReview

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

by Tianqi Zhao, Yongcheng Wang, Zheng Li, Yunxiao Gao, Chi Chen, Hao Feng and Zhikang Zhao

Remote Sens. 2024, 16(7), 1145; https://doi.org/10.3390/rs16071145 - 25 Mar 2024

Viewed by 710

Abstract

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)

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