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Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications

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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: closed (30 September 2023) | Viewed by 18268

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Special Issue Editors

Dr. Tianwen Zhang

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: computer vision; neural networks; object detection/classification/segmentation; remote sensing processing; synthetic aperture radar; millimeter wave radar technology
Special Issues, Collections and Topics in MDPI journals

Dr. Tianjiao Zeng

E-Mail Website
Guest Editor

School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Interests: computational imaging; inverse imaging problems; deep learning; neuroimaging
Special Issues, Collections and Topics in MDPI journals

Dr. Jun Shi

E-Mail Website
Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: image processing; radar signal processing; remote sensing applications; multisensor fusion

Dr. Shuaicheng Liu

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: computer vision; computer graphics; photography; optical image processing

Dr. Shunjun Wei

E-Mail Website
Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar system and sensing; SAR imaging and application; object identification
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qingze Zou

E-Mail Website
Guest Editor

Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ 08854, USA
Interests: mobile sensing; stomatal dynamics characterization and modeling; acoustic noise cancel-lation and control; sonar signal processing

Prof. Dr. Xiaoling Zhang

E-Mail Website
Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: interferometry synthetic aperture radar (InSAR); InSAR remote sensing; remote sensing processing; machine learning and deep learning; detection and classification using SAR images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A wide variety of sensors are used in remote sensing, working work together to achieve comprehensive, high-precision observations of the Earth. Radar sensors actively transmit electromagnetic waves, which can penetrate clouds and fog without being affected by light. Optical sensors can capture color information from ground objects and have better a visual observation effect. Hyperspectral sensors can detect substances with diagnostic spectral absorption characteristics and accurately distinguish between types of vegetation cover, roads, ground materials, etc., improving the accuracy and reliability of imaging quantitative analysis. Infrared sensors experience no electromagnetic interference and present good accuracy in tracking thermal targets from a long distance as well as in location and navigation functions. Sonar sensors work well under water and can thus realize ocean and river observation. Radar, optical, hyperspectral, infrared, and sonar all play an important role in remote sensing.

This Special Issue provides a platform for researchers to publish their studies and present innovative and cutting-edge research results with regard to the application of radar, optical, hyperspectral, infrared, and sonar to remote sensing, e.g., in data acquisition, processing, and applications. Potential topics include, but are not limited to:

Radar signal/image processing;
Optical signal/image processing;
Hyperspectral signal/image processing;
Infrared signal/image processing;
Sonar signal/image processing;
Multi-sensor Earth observation;
Multi-sensor applications in agriculture, oceans, ecology, and the environment;
Advanced sensor techniques;
Multi-sensor fusion.

Articles, Reviews, Letter, Technical Note, Communication, etc.

Dr. Tianwen Zhang
Dr. Tianjiao Zeng
Dr. Jun Shi
Dr. Shuaicheng Liu
Dr. Shunjun Wei
Prof. Dr. Qingze Zhou
Prof. Dr. Xiaoling Zhang
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

radar signal/image processing
optical signal/image processing
hyperspectral signal/image processing
infrared signal/image processing
sonar signal/image processing
multi-sensor earth observation
multi-sensor in agriculture, ocean, ecology, and environment
advanced sensor techniques
multi-sensor fusion

Published Papers (13 papers)

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Research

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21 pages, 3027 KiB

Open AccessArticle

CCRANet: A Two-Stage Local Attention Network for Single-Frame Low-Resolution Infrared Small Target Detection

by Wenjing Wang, Chengwang Xiao, Haofeng Dou, Ruixiang Liang, Huaibin Yuan, Guanghui Zhao, Zhiwei Chen and Yuhang Huang

Remote Sens. 2023, 15(23), 5539; https://doi.org/10.3390/rs15235539 - 28 Nov 2023

Cited by 1 | Viewed by 765

Abstract

Infrared small target detection technology is widely used in infrared search and tracking, infrared precision guidance, low and slow small aircraft detection, and other projects. Its detection ability is very important in terms of finding unknown targets as early as possible, warning in time, and allowing for enough response time for the security system. This paper combines the target characteristics of low-resolution infrared small target images and studies the infrared small target detection method under a complex background based on the attention mechanism. The main contents of this paper are as follows: (1) by sorting through and expanding the existing datasets, we construct a single-frame low-resolution infrared small target (SLR-IRST) dataset and evaluate the existing datasets on three aspects—target number, target category, and target size; (2) to improve the pixel-level metrics of low-resolution infrared small target detection, we propose a small target detection network with two stages and a corresponding method. Regarding the SLR-IRST dataset, the proposed method is superior to the existing methods in terms of pixel-level metrics and target-level metrics and has certain advantages in model processing speed. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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19 pages, 956 KiB

Open AccessArticle

SID-TGAN: A Transformer-Based Generative Adversarial Network for Sonar Image Despeckling

by Xin Zhou, Kun Tian, Zihan Zhou, Bo Ning and Yanhao Wang

Remote Sens. 2023, 15(20), 5072; https://doi.org/10.3390/rs15205072 - 23 Oct 2023

Cited by 1 | Viewed by 1072

Abstract

Sonar images are inherently affected by speckle noise, which degrades image quality and hinders image exploitation. Despeckling is an important pre-processing task that aims to remove such noise so as to improve the accuracy of analysis tasks on sonar images. In this paper, we propose a novel transformer-based generative adversarial network named SID-TGAN for sonar image despeckling. In the SID-TGAN framework, transformer and convolutional blocks are used to extract global and local features, which are further integrated into the generator and discriminator networks for feature fusion and enhancement. By leveraging adversarial training, SID-TGAN learns more comprehensive representations of sonar images and shows outstanding performance in speckle denoising. Meanwhile, SID-TGAN introduces a new adversarial loss function that combines image content, local texture style, and global similarity to reduce image distortion and information loss during training. Finally, we compare SID-TGAN with state-of-the-art despeckling methods on one image dataset with synthetic optical noise and four real sonar image datasets. The results show that it achieves significantly better despeckling performance than existing methods on all five datasets. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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20 pages, 9365 KiB

Open AccessArticle

Airborne Radio-Echo Sounding Data Denoising Using Particle Swarm Optimization and Multivariate Variational Mode Decomposition

by Yuhan Chen, Sixin Liu, Kun Luo, Lijuan Wang and Xueyuan Tang

Remote Sens. 2023, 15(20), 5041; https://doi.org/10.3390/rs15205041 - 20 Oct 2023

Viewed by 816

Abstract

Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational mode decomposition (VMD) algorithm. It processes data encompassing multiple channels. Determining the most effective component combination of the penalty parameter (α) and the number of intrinsic mode functions (IMFs) (K) is fundamental and affects the decomposition results. α and K in traditional MVMD are provided by subjective experience. We integrated the particle swarm optimization (PSO) algorithm to iteratively optimize these parameters—specifically, α and K—with high precision. This was then combined with the four quantitative parameters: energy entropy, signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and root-mean-square error (RMSE). The RES signal decomposition results were judged, and the most effective component combination for noise suppression was selected. We processed the airborne RES data from the East Antarctic ice sheet using the combined PSO–MVMD method. The results confirmed the quality of the proposed method in attenuating the RES signal noise, enhancing the weak signal of the ice base, and improving the SNR. This combined PSO–MVMD method may help to enhance weak signals in deeper parts of ice sheets and may be an effective tool for RES data interpretation. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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18 pages, 42830 KiB

Open AccessArticle

Improvement of pBRDF Model for Target Surface Based on Diffraction and Transmission Effects

by Qiang Fu, Xuanwei Liu, Di Yang, Juntong Zhan, Qing Liu, Su Zhang, Fang Wang, Jin Duan, Yingchao Li and Huilin Jiang

Remote Sens. 2023, 15(14), 3481; https://doi.org/10.3390/rs15143481 - 11 Jul 2023

Cited by 6 | Viewed by 959

Abstract

The polarised Bidirectional Reflectance Distribution Function (pBRDF) model relates the properties of target materials to the polarisation information of the incident and reflected light. The Priest–Germer (P-G) model was the first strictly pBRDF model to be officially released; however, some shortcomings remain. In this study, we first analyse the assumption framework of the P-G model, analyse the assumption framework to determine the imperfections in the framework, supplement the boundary conditions of the model for diffraction and transmission effects, and propose and construct a polarised pBTDF model based on the existing P-G model and parameter inversion; the output results of the model are compared with the experimental data through simulation. The results show that the intensity relative error and Degree of Linear Polarisation relative error of the target can be reduced by more than 40%, using the improved model, proving its accuracy and precision. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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16 pages, 5852 KiB

Open AccessArticle

Near-Field IPO for Analysis of EM Scattering from Multiple Hybrid Dielectric and Conductor Target and High Resolution Range Profiles

by Qingkuan Wang, Yijin Wang, Chuangming Tong, Zhaolong Wang, Ximin Li and Tong Wang

Remote Sens. 2023, 15(7), 1884; https://doi.org/10.3390/rs15071884 - 31 Mar 2023

Viewed by 1137

Abstract

Aiming at improving the accuracy and efficiency of scattering information from multiple targets in near-field regions, this paper proposes a near-field iterative physical optics (IPO) method based on a modified near-field Green’s function for the composite electromagnetic scattering analysis of multiple hybrid dielectric and conductor targets. According to the electric field and magnetic field integral equation, the electric and magnetic current were updated utilizing the Jacobi iteration method. Then, by introducing an expansion center lying in the neighborhood of the source point, Green’s function was modified for near-field scattering between multiple hybrid dielectric and conductor targets. To accelerate the implementation of the procedure, the multilevel fast multipole method, the fast far-field approximation, and parallel multicore programming were introduced. Numerical results indicate that there is good agreement between the results calculated by the near-field IPO method and MLFMM solver in commercial software FEKO while significantly reducing the computational burden. To fully exploit the scattering information, the high resolution range profiles (HRRP) of different targets under different conditions were analyzed, which can be further applied for automatic target detection and recognition. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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17 pages, 5649 KiB

Open AccessArticle

Infrared Small Marine Target Detection Based on Spatiotemporal Dynamics Analysis

by Chujia Dang, Zhengzhou Li, Congyu Hao and Qin Xiao

Remote Sens. 2023, 15(5), 1258; https://doi.org/10.3390/rs15051258 - 24 Feb 2023

Cited by 2 | Viewed by 1264

Abstract

It is a big challenge to detect and track small infrared marine targets in non-stationary and time-varying sea clutter because the signal is too strong to be estimated. Based on the phenomenon that sea clutter spreads not only in the temporal domain but also in the spatial domain, this paper proposes an infrared small marine target detection algorithm based on spatiotemporal dynamics analysis to improve the performances of sea clutter suppression and target detection. The moving sea clutter is modeled as the spatial-temporal phase space, and the dynamical parameters of the sea clutter in the spatiotemporal domain are extracted from the sea clutter image sequence. Afterwards, the temporal dynamics reconstruction function and the spatial dynamics reconstruction function are built based on these extracted dynamical parameters. Furthermore, the space-time coupling coefficient and the spatiotemporal dynamics reconstruction function are estimated by means of a radial basis function (RBF) neural network to reconstruct the propagation regularity of the moving sea clutter. Finally, the sea clutter is suppressed by subtracting the estimated image from the original image, and then the target is detected in the suppressed image using the constant false alarm rate (CFAR) criteria. Some experiments on the small marine target in various fluctuating sea clutter image sequences are induced, and the experimental results show that the proposed algorithm could achieve outstanding performances in sea clutter suppression and small target detection. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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19 pages, 12377 KiB

Open AccessArticle

On-Orbit Vicarious Radiometric Calibration and Validation of ZY1-02E Thermal Infrared Sensor

by Hongzhao Tang, Junfeng Xie, Xianhui Dou, Honggeng Zhang and Wei Chen

Remote Sens. 2023, 15(4), 994; https://doi.org/10.3390/rs15040994 - 10 Feb 2023

Cited by 1 | Viewed by 1254

Abstract

The ZY1-02E satellite carrying a thermal infrared sensor was successfully launched from the Taiyuan Satellite Launch Center on 26 December 2021. The quantitative characteristics of this thermal infrared camera, for use in supporting applications, were acquired as part of an absolute radiometric calibration campaign performed at the Ulansuhai Nur and Baotou calibration site (Inner Mongolia, July 2022). In this paper, we propose a novel on-orbit absolute radiometric calibration technique, based on multiple ground observations, that considers the radiometric characteristics of the ZY1-02E thermal infrared sensor. A variety of natural surface objects were selected as references, including bodies of water, bare soil, a desert in Kubuqi, and sand and vegetation at the Baotou calibration site. During satellite overpass, the 102F Fourier transform thermal infrared spectrometer and the SI-111 infrared temperature sensor were used to measure temperature and ground-leaving radiance for these surface profiles. Atmospheric water vapor, aerosol optical depth, and ozone concentration were simultaneously obtained from the CIMEL CE318 Sun photometer and the MICROTOP II ozonometer. Atmospheric profile information was acquired from radiosonde instruments carried by sounding balloons. Synchronous measurements of atmospheric parameters and ECMWF ERA5 reanalysis data were then combined and input to an atmospheric radiative transfer model (MODTRAN6.0) used to calculate apparent radiance. Calibration coefficients were determined from the measured apparent radiance and satellite-observed digital number (DN), for use in calculating the on-orbit observed radiance of typical surface objects. These values were then compared with the apparent radiance of each object, using radiative transfer calculations to evaluate the accuracy of on-orbit absolute radiometric calibration. The results show that the accuracy of this absolute radiometric calibration is better than 0.6 K. This approach allows the thermal infrared channel to be unrestricted by the limitations of spectrum matching between a satellite and field measurements, with strong applicability to various types of calibration sites. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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33 pages, 7164 KiB

Open AccessArticle

Scale in Scale for SAR Ship Instance Segmentation

by Zikang Shao, Xiaoling Zhang, Shunjun Wei, Jun Shi, Xiao Ke, Xiaowo Xu, Xu Zhan, Tianwen Zhang and Tianjiao Zeng

Remote Sens. 2023, 15(3), 629; https://doi.org/10.3390/rs15030629 - 20 Jan 2023

Cited by 6 | Viewed by 1858

Abstract

Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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19 pages, 4691 KiB

Open AccessArticle

Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification

by Ali Radman, Masoud Mahdianpari, Brian Brisco, Bahram Salehi and Fariba Mohammadimanesh

Remote Sens. 2023, 15(1), 75; https://doi.org/10.3390/rs15010075 - 23 Dec 2022

Cited by 3 | Viewed by 2077

Abstract

Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast to optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods can provide solutions to address PolSAR feature extraction challenges. The convolutional neural networks (CNNs) and graph convolutional networks (GCNs) can drive PolSAR image characteristics by deploying kernel abilities in considering neighborhood (local) information and graphs in considering long-range similarities. A novel dual-branch fusion of CNN and mini-GCN is proposed in this study for PolSAR image classification. To fully utilize the PolSAR image capacity, different spatial-based and polarimetric-based features are incorporated into CNN and mini-GCN branches of the proposed model. The performance of the proposed method is verified by comparing the classification results to multiple state-of-the-art approaches on the airborne synthetic aperture radar (AIRSAR) dataset of Flevoland and San Francisco. The proposed approach showed 1.3% and 2.7% improvements in overall accuracy compared to conventional methods with these AIRSAR datasets. Meanwhile, it enhanced its one-branch version by 0.73% and 1.82%. Analyses over Flevoland data further indicated the effectiveness of the dual-branch model using varied training sampling ratios, leading to a promising overall accuracy of 99.9% with a 10% sampling ratio. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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22 pages, 12574 KiB

Open AccessArticle

Infrared Maritime Small Target Detection Based on Multidirectional Uniformity and Sparse-Weight Similarity

by Enzhong Zhao, Lili Dong and Hao Dai

Remote Sens. 2022, 14(21), 5492; https://doi.org/10.3390/rs14215492 - 31 Oct 2022

Cited by 6 | Viewed by 1647

Abstract

Infrared maritime target detection is a key technology in the field of maritime search and rescue, which usually requires high detection accuracy. Despite the promising progress of principal component analysis methods, it is still challenging to detect small targets of unknown polarity (bright [...] Read more.

l_{1}

norm to estimate sparse components is an effective method for target extraction. In order to suppress the strong edge interference, considering that the uniformity of the target scattering field is significantly higher than that of the background scattering field in the eigenvalue of the structure tensor, a prior weight based on the multidirectional uniformity of structure tensor eigenvalue was proposed and applied to the optimization model. In order to detect targets with unknown polarity, the images with opposite polarity were substituted into the optimization model, respectively, and the sparse-weight similarity is used to judge the polarity of the target. In order to make the method more efficient, the polarity judgment is made in the second iteration, and then, the false iteration will stop. The proposed method is compared with nine advanced baseline methods on 14 datasets and shows significant strong robustness, which is beneficial to engineering applications. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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23 pages, 4240 KiB

Open AccessArticle

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

by Jinming Du, Huanzhang Lu, Luping Zhang, Moufa Hu, Yingjie Deng, Xinglin Shen, Dongyang Li and Yu Zhang

Remote Sens. 2022, 14(20), 5072; https://doi.org/10.3390/rs14205072 - 11 Oct 2022

Cited by 3 | Viewed by 1596

Abstract

The detection and tracking of small targets under low signal-to-clutter ratio (SCR) has been a challenging task for infrared search and track (IRST) systems. Track-before-detect (TBD) is a widely-known algorithm which can solve this problem. However, huge computation costs and storage requirements limit its application. To address these issues, a dynamic programming (DP) and multiple hypothesis testing (MHT)-based infrared dim point target detection algorithm (DP–MHT–TBD) is proposed. It consists of three parts. (1) For each pixel in current frame, the second power optimal merit function-based DP is designed and performed in eight search areas to find the target search area that contains the real target trajectory. (2) In the target search area, the parallel MHT model is designed to save the tree-structured trajectory space, and a two-stage strategy is designed to mitigate the contradiction between the redundant trajectories and the requirements of more trajectories under low SCR. After constant false alarm segmentation of the energy accumulation map, the preliminary candidate points can be obtained. (3) The target tracking method is designed to eliminate false alarms. In this work, an efficient second power optimal merit function-based DP is designed to find the target search area for each pixel, which greatly reduces the trajectory search space. A two-stage MHT model, in which pruning for the tree-structured trajectory space is avoided and all trajectories can be processed in parallel, is designed to further reduce the hypothesis space exponentially. This model greatly reduces computational complexity and saves storage space, improving the engineering application of the TBD method. The DP–MHT–TBD not only takes advantage of the small computation amount of DP and high accuracy of an exhaustive search but also utilizes a novel structure. It can detect a single infrared point target when the SCR is 1.5 with detection probability above 90% and a false alarm rate below 0.01%. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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19 pages, 6600 KiB

Open AccessArticle

A New Strategy for Forest Height Estimation Using Airborne X-Band PolInSAR Data

by Jinwei Xie, Lei Li, Long Zhuang and Yu Zheng

Remote Sens. 2022, 14(19), 4743; https://doi.org/10.3390/rs14194743 - 22 Sep 2022

Cited by 1 | Viewed by 1174

Abstract

Because the penetration depth of electromagnetic waves in forests is large in the longer wavelength band, most traditional forest height estimation methods are carried out using polarimetric interferometry synthetic aperture radar (PolInSAR) data of the L or P band, and the estimation method is a three-stage method based on the random volume over ground (RVoG) model. For X-band electromagnetic waves, the penetration depth of radar waves in forests is limited, so the traditional forest height estimation method is no longer applicable. In view of the above problems, in this paper we propose a new forest height estimation strategy for airborne X-band PolInSAR data. Firstly, the sub-view interferometric SAR pairs obtained via frequency segmentation (FS) in the Doppler domain are used to extend the polarimetric interferometry coherence coefficient (PolInCC) range of the original SAR image under different polarization states, so as to obtain the accurate ground phase. For the determination of the effective volume coherence coefficient (VCC), part of the fitting line of the extended-range PolInCC distribution that is intercepted by the fixed extinction coherence coefficient curve (FECCC) of the fixed range is averaged to obtain the accurate effective VCC. Finally, the high-precision forest canopy height in the X-band is estimated using the effective VCC with the ground phase removed in the look-up table (LUT). The effectiveness of the proposed method was verified using airborne-measured data obtained in Shaanxi Province, China. The comparison was carried out using different strategies, in which we substituted one step of the process with the conventional method. The results indicated that our new strategy could reduce the root mean square error (RMSE) of the predicted canopy height vastly to 1.02 m, with a lower estimation height error of 12.86%. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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15 pages, 33791 KiB

Open AccessTechnical Note

On-Board Geometric Rectification for Micro-Satellite Based on Lightweight Feature Database

by Linhui Wang, Yuming Xiang, Zhenzhou Wang, Hongjian You and Yuxin Hu

Remote Sens. 2023, 15(22), 5333; https://doi.org/10.3390/rs15225333 - 12 Nov 2023

Cited by 1 | Viewed by 820

Abstract

On-board processing is increasingly prevalent due to its efficient utilization of satellite resources. Among these resources, geometric rectification can significantly enhance positioning accuracy for subsequent tasks, such as object detection. This approach mitigates the heavy burden on downlink bandwidth and minimizes time delays by transmitting targeted patches rather than raw data. However, existing rectification methods are often unsuitable due to the limitations and conditions imposed on satellites. Factors like hardware quality, heat dissipation, storage space, and geographic positioning are frequently constrained and prone to inaccuracies. This paper proposes a novel on-board rectification method. The method introduces a two-step matching framework to address substantial positioning errors and incorporates a feature-compression strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate the method’s efficacy in terms of storage space, time efficiency, and geometric rectification accuracy. Full article

(This article belongs to the Special Issue Advances in Radar, Optical, Hyperspectral, Infrared, and Sonar Technology: Data Acquisition, Processing, and Applications)

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