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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: closed (9 February 2024) | Viewed by 19401

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Dr. Shaohui Mei

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School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
Interests: remote sensing image processing; pattern recognition and machine learning
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Dr. Xiuping Jia

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School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
Interests: photogrammetry and remote sensing; image processing
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Prof. Dr. Ran Tao

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School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: multi-dimensional signal/image precessing
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Dr. Junhui Hou

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Guest Editor

Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong 999077, China
Interests: image/video representations and analysis; semi-supervised/unsupervised data modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing theory and methods play an important role in the development of remote sensing technologies. The scientists working on signal processing-related topics always bring new ideas that are often successfully applied in the processes of remote sensing, including data acquisition, compression, transformation, processing, and application. From conventional Fourier transformation to the latest deep neural networks, every development in signal processing areas certainly promotes the rapid growth of remote sensing technologies, and even motivates new remote sensing technologies. Every development of remote sensing technologies is inseparable from the theory and method of signal processing.

This Special Issue aims at studies covering signal processing theory and methods that are used in different stages of different kinds of remote sensing technologies. Topics may cover anything from classical signal processing theories (e.g., Fourier transformation and Wavelet transformation) to the latest intelligent signal processing theories (e.g., deep neural network). Hence, signal processing theory and methods that are used in the acquisition, compression, transmission, processing, and application of both optical and microwave remote sensing are welcome. Articles may address, but are not limited, to the following topics:

Signal estimation in remote sensing;
Signal reconstruction in remote sensing;
Signal processing hardware for remote sensing;
Signal filtering in remote sensing;
Signal detection in remote sensing;
Signal identification in remote sensing;
Fourier Transform-based remote sensing;
Fractional Fourier Transform-based remote sensing;
Wavelet-based remote sensing;
Compressive sensing-based remote sensing;
Neural network-based remote sensing;
Statistic signal processing for remote sensing;
Intelligent signal processing for remote sensing.

Dr. Shaohui Mei
Dr. Xiuping Jia
Prof. Dr. Ran Tao
Dr. Junhui Hou
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

signal processing
remote sensing
Fourier transform
wavelet
compressive sensing
neural network
statistic signal processing
intelligent signal processing

Related Special Issue

Signal Processing Theory and Methods in Remote Sensing (Second Edition) in Remote Sensing

Published Papers (12 papers)

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23 pages, 2148 KiB

Open AccessArticle

MRG-T: Mask-Relation-Guided Transformer for Remote Vision-Based Pedestrian Attribute Recognition in Aerial Imagery

by Shun Zhang, Yupeng Li, Xiao Wu, Zunheng Chu and Lingfei Li

Remote Sens. 2024, 16(7), 1216; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16071216 - 29 Mar 2024

Viewed by 497

Abstract

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), utilizing UAV platforms for visual surveillance has become very attractive, and a key part of this is remote vision-based pedestrian attribute recognition. Pedestrian Attribute Recognition (PAR) is dedicated to predicting multiple attribute labels of a single pedestrian image extracted from surveillance videos and aerial imagery, which presents significant challenges in the computer vision community due to factors such as poor imaging quality and substantial pose variations. Despite recent studies demonstrating impressive advancements in utilizing complicated architectures and exploring relations, most of them may fail to fully and systematically consider the inter-region, inter-attribute, and region-attribute mapping relations simultaneously and be stuck in the dilemma of information redundancy, leading to the degradation of recognition accuracy. To address the issues, we construct a novel Mask-Relation-Guided Transformer (MRG-T) framework that consists of three relation modeling modules to fully exploit spatial and semantic relations in the model learning process. Specifically, we first propose a Masked Region Relation Module (MRRM) to focus on precise spatial attention regions to extract more robust features with masked random patch training. To explore the semantic association of attributes, we further present a Masked Attribute Relation Module (MARM) to extract intrinsic and semantic inter-attribute relations with an attribute label masking strategy. Based on the cross-attention mechanism, we finally design a Region and Attribute Mapping Module (RAMM) to learn the cross-modal alignment between spatial regions and semantic attributes. We conduct comprehensive experiments on three public benchmarks such as PETA, PA-100K, and RAPv1, and conduct inference on a large-scale airborne person dataset named PRAI-1581. The extensive experimental results demonstrate the superior performance of our method compared to state-of-the-art approaches and validate the effectiveness of mask-relation-guided modeling in the remote vision-based PAR task. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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24 pages, 6495 KiB

Open AccessArticle

The Synchrosqueezed Method and Its Theory-Analysis-Based Novel Short-Time Fractional Fourier Transform for Chirp Signals

by Zhen Li, Zhaoqi Gao, Liang Chen, Jinghuai Gao and Zongben Xu

Remote Sens. 2024, 16(7), 1173; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16071173 - 27 Mar 2024

Viewed by 452

Abstract

Time–frequency analysis is an important tool used for the processing and interpretation of non-stationary signals, such as seismic data and remote sensing data. In this paper, based on the novel short-time fractional Fourier transform (STFRFT), a new modified STFRFT is first proposed which can also generalize the properties of the modified short-time Fourier transform (STFT). Then, in the modified STFRFT domain, we derive the instantaneous frequency estimator for the chirp signal and present a new type of synchrosqueezing STFRFT (FRSST). The proposed FRSST presents many results similar to those of the synchrosqueezing STFT (FSST), and it extends the harmonic signal to a chirp signal that offers attractive new features. Furthermore, we provide a detailed analysis of the signal reconstruction, theories, and some properties of the proposed FRSST. Several experiments are conducted, and all of the results illustrate that the proposed FRSST is more effective than the FSST. Finally, based on the linear amplitude modulation and frequency modulation signal, we present a derivation for analyzing the limitations of the FRSST. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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18 pages, 20243 KiB

Open AccessArticle

Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition

by Kang Xing, Shiyan Li, Zhijie Qu and Xiaojuan Zhang

Remote Sens. 2024, 16(5), 806; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050806 - 25 Feb 2024

Viewed by 599

Abstract

Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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21 pages, 848 KiB

Open AccessArticle

A Novel Non-Stationary Clutter Suppression Approach for Space-Based Early Warning Radar Using an Interpulse Multi-Frequency Mode

by Ning Qiao, Shuangxi Zhang, Shuo Zhang, Qinglei Du and Yongliang Wang

Remote Sens. 2024, 16(2), 314; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16020314 - 12 Jan 2024

Viewed by 587

Abstract

The non-stationary clutter of space-based early warning radar (SBEWR) is more serious than that of airborne early warning radar. This phenomenon is primarily attributed to the Earth’s rotation and range ambiguity. The increase in clutter degrees of freedom (DOFs) and the significant widening of the clutter suppression notch are not conducive to moving target detection near main lobe clutter. This paper proposes an effective approach to suppress non-stationary clutter based on an interpulse multi-frequency mode for SBEWR. Using the orthogonality of the uniform stepping frequency signal, partial range ambiguity can be effectively suppressed, and the clutter DOFs will be reduced. Subsequently, joint pitch-azimuth-Doppler three-dimensional spacetime adaptive processing and slant range preprocessing are used to perform clutter suppression. This combination not only curtails the estimation error associated with the clutter covariance matrix but also enhances the overall detection capabilities of the system. The simulation results verify the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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26 pages, 10296 KiB

Open AccessArticle

A Spatial–Temporal Joint Radar-Communication Waveform Design Method with Low Sidelobe Level of Beampattern

by Liu Liu, Xingdong Liang, Yanlei Li, Yunlong Liu, Xiangxi Bu and Mingming Wang

Remote Sens. 2023, 15(4), 1167; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041167 - 20 Feb 2023

Cited by 2 | Viewed by 1711

Abstract

A joint radar-communication (JRC) system utilizes the integrated transmit waveform and a single platform to perform radar and communication functions simultaneously. Admittedly, the multibeam waveform design approach could transmit the assigned waveforms in different beams with the aid of spatial and temporal degrees of freedom. However, a high sidelobe level (SLL) in the beampattern reduces energy efficiency and expands exposure probability. In this study, we propose a novel spatial–temporal joint waveform design method based on the beamforming algorithm to form a low SLL beampattern. Waveform synthesis constraints are considered to synthesize desired radar and communication waveforms at designated directions. Furthermore, we impose the constant modulus constraint to lessen the impact of the high peak-to-average ratio (PAPR). The optimization process of the whole model can be summarized as two stages. First, the covariance matrix is created by convex optimization with respect to the minimum SLL. Second, the integrated transmit waveform is tuned through an alternating projection algorithm. Based on the simulation findings, we demonstrate that the proposed method outperforms the traditional methods in terms of low SLL and waveform synthesis. Meanwhile, we validate the effectiveness of the proposed method using semi-physical experiment results. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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20 pages, 2079 KiB

Open AccessArticle

BiTSRS: A Bi-Decoder Transformer Segmentor for High-Spatial-Resolution Remote Sensing Images

by Yuheng Liu, Yifan Zhang, Ye Wang and Shaohui Mei

Remote Sens. 2023, 15(3), 840; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030840 - 02 Feb 2023

Cited by 5 | Viewed by 1844

Abstract

Semantic segmentation of high-spatial-resolution (HSR) remote sensing (RS) images has been extensively studied, and most of the existing methods are based on convolutional neural network (CNN) models. However, the CNN is regarded to have less power in global representation modeling. In the past few years, methods using transformer have attracted increasing attention and generate improved results in semantic segmentation of natural images, owing to their powerful ability in global information acquisition. Nevertheless, these transformer-based methods exhibit limited performance in semantic segmentation of RS images, probably because of the lack of comprehensive understanding in the feature decoding process. In this paper, a novel transformer-based model named the bi-decoder transformer segmentor for remote sensing (BiTSRS) is proposed, aiming at alleviating the problem of flexible feature decoding, through a bi-decoder design for semantic segmentation of RS images. In the proposed BiTSRS, the Swin transformer is adopted as encoder to take both global and local representations into consideration, and a unique design module (ITM) is designed to deal with the limitation of input size for Swin transformer. Furthermore, BiTSRS adopts a bi-decoder structure consisting of a Dilated-Uper decoder and a fully deformable convolutional network (FDCN) module embedded with focal loss, with which it is capable of decoding a wide range of features and local detail deformations. Both ablation experiments and comparison experiments were conducted on three representative RS images datasets. The ablation analysis demonstrates the contributions of specifically designed modules in the proposed BiTSRS to performance improvement. The comparison experimental results illustrate that the proposed BiTSRS clearly outperforms some state-of-the-art semantic segmentation methods. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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19 pages, 4408 KiB

Open AccessArticle

A Remote-Vision-Based Safety Helmet and Harness Monitoring System Based on Attribute Knowledge Modeling

by Xiao Wu, Yupeng Li, Jihui Long, Shun Zhang, Shuai Wan and Shaohui Mei

Remote Sens. 2023, 15(2), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020347 - 06 Jan 2023

Cited by 2 | Viewed by 3205

Abstract

Remote-vision-based image processing plays a vital role in the safety helmet and harness monitoring of construction sites, in which computer-vision-based automatic safety helmet and harness monitoring systems have attracted significant attention for practical applications. However, many problems have not been well solved in existing computer-vision-based systems, such as the shortage of safety helmet and harness monitoring datasets and the low accuracy of the detection algorithms. To address these issues, an attribute-knowledge-modeling-based safety helmet and harness monitoring system is constructed in this paper, which elegantly transforms safety state recognition into images’ semantic attribute recognition. Specifically, a novel transformer-based end-to-end network with a self-attention mechanism is proposed to improve attribute recognition performance by making full use of the correlations between image features and semantic attributes, based on which a security recognition system is constructed by integrating detection, tracking, and attribute recognition. Experimental results for safety helmet and harness detection demonstrate that the accuracy and robustness of the proposed transformer-based attribute recognition algorithm obviously outperforms the state-of-the-art algorithms, and the presented system is robust to challenges such as pose variation, occlusion, and a cluttered background. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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22 pages, 3256 KiB

Open AccessArticle

Towards Single-Component and Dual-Component Radar Emitter Signal Intra-Pulse Modulation Classification Based on Convolutional Neural Network and Transformer

by Shibo Yuan, Peng Li and Bin Wu

Remote Sens. 2022, 14(15), 3690; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153690 - 01 Aug 2022

Cited by 5 | Viewed by 1783

Abstract

In the modern electromagnetic environment, the intra-pulse modulations of radar emitter signals have become more complex. Except for the single-component radar signals, dual-component radar signals have been widely used in the current radar systems. In order to make the radar system have the ability to classify single-component and dual-component intra-pulse modulation at the same period of time accurately, in this paper, we propose a multi-label learning method based on a convolutional neural network and transformer. Firstly, the original single channel sampled sequences are padded with zeros to the same length. Then the padded sequences are converted to frequency-domain sequences that only contain the amplitude information. After that, data normalization is employed to decrease the influence of amplitude. After radar signals preprocessing, a designed model which combines a convolutional neural network and transformer is used to accomplish multi-label classification. The extensive experiments indicate that the proposed method consumes lower computation resources and has higher accuracy than other baseline methods in classifying eight types of single and thirty-six types of dual-component intra-pulse modulation, where the overall accuracy and weighted accuracy are beyond 90%. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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20 pages, 12409 KiB

Open AccessArticle

Hyperspectral Image Classification via Deep Structure Dictionary Learning

by Wenzheng Wang, Yuqi Han, Chenwei Deng and Zhen Li

Remote Sens. 2022, 14(9), 2266; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092266 - 08 May 2022

Cited by 14 | Viewed by 2148

Abstract

The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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27 pages, 3067 KiB

Open AccessArticle

Non-Line-of-Sight Moving Target Detection Method Based on Noise Suppression

by Yilin Wei, Bing Sun, Yuetong Zhou and Haochuan Wang

Remote Sens. 2022, 14(7), 1614; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071614 - 28 Mar 2022

Cited by 7 | Viewed by 2689

Abstract

At the present time, most of existing security systems only detect and track targets in line-of-sight (LOS). However, in practice, the locations of targets are often out of the line of sight. This article focuses on the non-line-of-sight (NLOS) moving target detection with low-power transmission signals by reflection. There are two key problems, the weak target echo signal and the multipath effect. In terms of the issues, this paper constructs the echo signal model of the NLOS target. On the basis of the echo model, the detection method of NLOS moving target based on millimeter-wave radar comes up, which is of great theoretical value and important practical significance for indoor security. This paper innovatively applies the polynomial fitting method to suppress static noise and range gating method to suppress noise from other range gates. Then, the location and velocity of the target are estimated by two-dimensional fast Fourier transform (FFT) and the multiple signal classification (MUSIC) method. Furthermore, in order to verify the accuracy of the NLOS target echo signal model proposed in this paper, we respectively simulated two important parts of the signal in the model, the target echo signal and the direct backscattered signal of the intermediate interface, both of which are multipath signals. We counted the echo path length distribution in these two parts, and applied the NLOS target detection method to process them respectively. In addition, we also simulated the NLOS target echo signal and obtained actual data in the actual scene, and processed both the simulated data and the actual data. Comparing the results of target detection with and without denoising methods, the effectiveness of the two denoising methods proposed in this paper is verified. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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17 pages, 3857 KiB

Open AccessArticle

The Short-Arc Precise Orbit Determination of GEO Satellites Using VLBI and Transfer Ranging

by Kai Nan, Fen Cao, Jianjun Gong, Hui Lei, Zhigang Li and Xuhai Yang

Remote Sens. 2022, 14(7), 1572; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071572 - 24 Mar 2022

Viewed by 1699

Abstract

It is important for a geostationary Earth orbit (GEO) satellite to rapidly recover its orbit after a maneuver with short-arc precise orbit determination (POD). Based on orbit determination by transfer tracking (ODTT), the POD accuracy of a GEO satellite is less than 10 m over a short arc. ODTT can achieve high accuracy in the radial direction but is weak in the transverse direction. Considering that very long baseline interferometry (VLBI) can reduce the value of position dilution of precision (PDOP), especially in the transverse direction, a joint POD method using both VLBI and ODTT is proposed herein to improve POD accuracy and rapidly recover the orbit. An ODTT system and the first VLBI 2010 Global Observation System (VGOS) in China was used to track the ZX 12# GEO satellite. The results showed that the ODTT POD accuracy was 3.016 and 2.707 m for 2 and 4 h arcs, respectively. When using both VLBI and ODTT, the POD accuracy was 2.658 m for the 2 h arc, an improvement of 11.87% compared to the POD using ODTT alone. Therefore, VLBI and ODTT can be used together to increase the short-arc POD accuracy while also reducing the arc length necessary to recover the orbit. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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13 pages, 4931 KiB

Open AccessTechnical Note

Seismic Data Reconstruction Using a Phase-Shift-Plus-Interpolation-Based Apex-Shifted Hyperbolic Radon Transform

by Yue Wang, Xiangbo Gong and Bin Hu

Remote Sens. 2024, 16(7), 1114; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16071114 - 22 Mar 2024

Viewed by 477

Abstract

The apex-shifted hyperbolic Radon transform (ASHRT) based on the Stolt-stretch operator can be implemented in the frequency domain, which accelerates the computation efficiency of ASHRT. However, the Stolt-stretch operator has limitations when it comes to velocity variations. Therefore, this paper introduces a new ASHRT approach based on post-stack phase shift plus interpolation (PSPI) imaging and modeling operators. This new approach is designed to better adapt to changes in medium velocity and enhance the quality of data reconstruction. When combining this novel transformation with sparsity constraints for model testing and real data applications, the experimental results indicate that it is an effective data reconstruction tool, with superior data reconstruction results compared to traditional ASHRT based on the Stolt-stretch operator. Full article

(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing)

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