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Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 7310

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Special Issue Editors

Prof. Dr. Chenwei Deng

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Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing 100811, China
Interests: object detection; object recognition; object tracking

Dr. Wenzheng Wang

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Guest Editor

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: hyperspectral image processing; multi-model fusion
Special Issues, Collections and Topics in MDPI journals

Dr. Jeongho Cho

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Guest Editor

Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
Interests: object detection and tracking; signal clustering; integrity monitoring

Special Issue Information

Dear Colleagues,

Autonomous object detection, recognition and tracking, as important research topics in remote sensing field, have excellent potential in aerial reconnaissance, environmental perception and management, disaster monitoring, aerial photography and other related applications. Currently, most of the existing algorithms employ a single sensor (e.g., visible light, radar, infrared, etc.) to capture the original images. However, a single sensor can only represent the target from certain specific dimensions. For instance, infrared images are imaged according to the object’s thermal radiation without external light sources, whereas depth sensors can provide 3D position information for the objects. Radar can provide microwave reflection characteristics for the object, despite adverse climatic conditions. Currently, with the rapid development of hardware systems and intelligent platforms, various sensors are integrated into satellite and UAV platforms, allowing for the utilization of their complementary and redundant characteristics. Therefore, in order to achieve and accurate and robust perception system, this Special Issue will focus on advances in multiple sensor fusion and classification for object detection and tracking.

Potential topics for this Special Issue include, but are not limited to:

New detection and tracking models with multiple sources/multi-modal information in remote sensing;
Efficient target feature representation with multiple modalities;
Multi-modal remote sensing data fusion, analysis and understanding;
Large-scale multiple sensors/multiple modalities data compressing and transmission;
New benchmark datasets including multi-source, multi-modal or multi-dimensional information fusion for object detection, tracking and recognition in remote sensing tasks；
Emerging remote sensing applications with multi-source, or multi-dimensional information fusion;
Land cover classification and change detection methods based on multi-source data fusion in remote sensing;
Domain adaptive data analysis and understanding in remote sensing

Prof. Dr. Chenwei Deng
Dr. Wenzheng Wang
Dr. Jeongho Cho
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 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

multi-modal, multi-dimensional, multi-source information fusion
object detection, recognition and tracking
earth observation applications
feature representation and modeling
machine learning and deep learning

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Published Papers (7 papers)

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Research

17 pages, 8385 KiB

Open AccessArticle

Noise Radar Waveform Design Using Evolutionary Algorithms and Negentropy Constraint

by Afonso L. Sénica, Paulo A. C. Marques and Mário A. T. Figueiredo

Remote Sens. 2025, 17(8), 1327; https://doi.org/10.3390/rs17081327 - 8 Apr 2025

Viewed by 277

Abstract

In recent years, several advantages of noise radars have positioned this technology as a promising alternative to conventional radar technology. Immunity to jamming, low mutual interference, and low probability of interception are good examples of these advantages. However, the nature of random sequences introduces several issues, such as fluctuations in the range sidelobes of the autocorrelation function causing high sidelobe levels, hence not exploitable by radar systems. This study introduces the use of multi-objective evolutionary (MOE) algorithms to design noise radar waveforms with good autocorrelation properties as well as a low peak-to-average power ratio (PAPR). A set of Pareto-optimal waveforms are produced and, most importantly, entropy is introduced as a constraint in order to maintain the transmitted signal close to a full non-deterministic waveform. Moreover, a relation between PAPR and negentropy (negative entropy) is established theoretically and validated with other authors’ simulations. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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20 pages, 7029 KiB

Open AccessArticle

Tracking of Low Radar Cross-Section Super-Sonic Objects Using Millimeter Wavelength Doppler Radar and Adaptive Digital Signal Processing

by Yair Richter, Shlomo Zach, Maxi Y. Blum, Gad A. Pinhasi and Yosef Pinhasi

Remote Sens. 2025, 17(4), 650; https://doi.org/10.3390/rs17040650 - 14 Feb 2025

Viewed by 524

Abstract

Small targets with low radar cross-section (RCS) and high velocities are very hard to track by radar as long as the frequent variations in speed and location demand shorten the integration temporal window. In this paper, we propose a technique for tracking evasive targets using a continuous wave (CW) radar array of multiple transmitters operating in the millimeter wavelength (MMW). The scheme is demonstrated to detect supersonic moving objects, such as rifle projectiles, with extremely short integration times while utilizing an adaptive processing algorithm of the received signal. Operation at extremely high frequencies qualifies spatial discrimination, leading to resolution improvement over radars operating in commonly used lower frequencies. CW transmissions result in efficient average power utilization and consumption of narrow bandwidths. It is shown that although CW radars are not naturally designed to estimate distances, the array arrangement can track the instantaneous location and velocity of even supersonic targets. Since a CW radar measures the target velocity via the Doppler frequency shift, it is resistant to the detection of undesired immovable objects in multi-scattering scenarios; thus, the tracking ability is not impaired in a stationary, cluttered environment. Using the presented radar scheme is shown to enable the processing of extremely weak signals that are reflected from objects with a low RCS. In the presented approach, the significant improvement in resolution is beneficial for the reduction in the required detection time. In addition, in relation to reducing the target recording time for processing, the presented scheme stimulates the detection and tracking of objects that make frequent changes in their velocity and position. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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23 pages, 11445 KiB

Open AccessArticle

Distributed Target Detection with Coherent Fusion in Tracking Based on Phase Prediction

by Aoya Wang, Jing Lu, Shenghua Zhou and Linhai Wang

Remote Sens. 2024, 16(24), 4779; https://doi.org/10.3390/rs16244779 - 21 Dec 2024

Viewed by 847

Abstract

In distributed radar, a coherent system often gains attention for its higher detection potential in contrast to its non-coherent counterpart. However, even for a distributed coherent radar, it is difficult to coherently accumulate local observations in the searching mode if target returns in local channels are decorrelated. In order to obtain the superiority of coherent processing while overcoming the real implementation difficulties of a coherent framework, this paper studies a distributed coherent detection algorithm for fusion detection. It is utilized in detecting a target during tracking while a target is searched for in a non-coherent manner. From historic observations on target tracking, relative phase delays in different channels are predicted by a phase lock loop and then used to compensate phases for observations in the current frame. Moreover, to enhance the detection performance of distributed radar during tracking, a switching rule between phase prediction-based coherent and non-coherent processing is proposed based on their detection performance. Numerical results indicate that the switching operation can improve the detection probability during tracking, and the non-coherent operation can still provide a moderate detection performance if the phase prediction is unreliable. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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19 pages, 2630 KiB

Open AccessArticle

Enhancing Long-Term Robustness of Inter-Space Laser Links in Space Gravitational Wave Detection: An Adaptive Weight Optimization Method for Multi-Attitude Sensors Data Fusion

by Zhao Cui, Xue Wang, Jinke Yang, Haoqi Shi, Bo Liang, Xingguang Qian, Zongjin Ye, Jianjun Jia, Yikun Wang and Jianyu Wang

Remote Sens. 2024, 16(22), 4179; https://doi.org/10.3390/rs16224179 - 8 Nov 2024

Viewed by 625

Abstract

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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20 pages, 13452 KiB

Open AccessArticle

Cadastral-to-Agricultural: A Study on the Feasibility of Using Cadastral Parcels for Agricultural Land Parcel Delineation

by Han Sae Kim, Hunsoo Song and Jinha Jung

Remote Sens. 2024, 16(19), 3568; https://doi.org/10.3390/rs16193568 - 25 Sep 2024

Viewed by 1169

Abstract

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an

F_{1}

-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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22 pages, 35836 KiB

Open AccessArticle

Masked Image Modeling Auxiliary Pseudo-Label Propagation with a Clustering Central Rectification Strategy for Cross-Scene Classification

by Xinyi Zhang, Yin Zhuang, Tong Zhang, Can Li and He Chen

Remote Sens. 2024, 16(11), 1983; https://doi.org/10.3390/rs16111983 - 31 May 2024

Cited by 2 | Viewed by 994

Abstract

Cross-scene classification focuses on setting up an effective domain adaptation (DA) way to transfer the learnable knowledge from source to target domain, which can be reasonably achieved through the pseudo-label propagation procedure. However, it is hard to bridge the objective existing severe domain discrepancy between source and target domains, and thus, there are several unreliable pseudo-labels generated in target domain and involved into pseudo-label propagation procedure, which would lead to unreliable error accumulation to deteriorate the performance of cross-scene classification. Therefore, in this paper, a novel Masked Image Modeling Auxiliary Pseudo-Label Propagation called

{MIM-AP}^{2}

with clustering central rectification strategy is proposed to improve the quality of pseudo-label propagation for cross-scene classification. First, in order to gracefully bridge the domain discrepancy and improve DA representation ability in-domain, a supervised class-token contrastive learning is designed to find the more consistent contextual clues to achieve knowledge transfer learning from source to target domain. At the same time, it is also incorporated with a self-supervised MIM mechanism according to a low random masking ratio to capture domain-specific information for improving the discriminability in-domain, which can lay a solid foundation for high-quality pseudo-label generation. Second, aiming to alleviate the impact of unreliable error accumulation, a clustering central rectification strategy is designed to adaptively update robustness clustering central representations to assist in rectifying unreliable pseudo-labels and learning a superior target domain specific classifier for cross-scene classification. Finally, extensive experiments are conducted on six cross-scene classification benchmarks, and the results are superior to other DA methods. The average accuracy reached 95.79%, which represents a 21.87% improvement over the baseline. This demonstrates that the proposed

{MIM-AP}^{2}

can provide significantly improved performance. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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27 pages, 4998 KiB

Open AccessArticle

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

by Xin Chen, Hao Zhang, Shenghao Zhang, Jiapeng Feng, Hui Xia, Peng Rao and Jianliang Ai

Remote Sens. 2024, 16(3), 510; https://doi.org/10.3390/rs16030510 - 29 Jan 2024

Cited by 6 | Viewed by 1625

Abstract

Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets. Full article

(This article belongs to the Special Issue Advances in Multiple Sensor Fusion and Classification for Object Detection and Tracking)

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