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

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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: 1 May 2025 | Viewed by 14456

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Special Issue Editors

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
Special Issues, Collections and Topics in MDPI journals

Dr. Junhui Hou

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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
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Dr. Kun Hu

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School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: multimedia computing; remote sensing applications; graph learning

Dr. Mingyang Ma

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Guest Editor

School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: remote sensing image processing; video understanding
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Special Issue Information

Dear Colleagues,

The development of signal processing is closely intertwined with the advancement of remote sensing. The continual evolution of signal processing techniques provides essential tools for the processing and interpretation of remote sensing data, while the development of remote sensing technology offers rich data sources and practical application scenarios for signal processing. With the progress of remote sensing technology, the volume and variety of data acquired have steadily increased. The development of signal processing techniques provides robust tools and methods for handling these diverse data, including image processing, time-series analysis, feature extraction, pattern recognition, and more. Thus, the development of signal processing and remote sensing mutually reinforce each other, collectively driving the widespread application of remote sensing technology in fields such as earth science, environmental monitoring, and resource management.

This Special Issue aims to explore the latest advancements and innovative applications of signal processing theory and methods in remote sensing. We invite contributions focusing on innovative signal processing techniques for the enhancement, analysis, and interpretation of remote sensing data across different domains. Topics of interest include, but are not limited to, the following:

Feature extraction for remote sensing;
Time-series analysis for remote sensing observations;
Fusion techniques for multi-source remote sensing data;
Real-time signal processing for remote sensing;
Large-scale image processing for remote sensing;
Compressed sensing for remote sensing;
Deep learning approaches for remote sensing;
Signal processing platforms for remote sensing;
Signal processing in remote sensing applications.

Dr. Shaohui Mei
Dr. Junhui Hou
Dr. Kun Hu
Dr. Mingyang Ma
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
image processing
deep learning
information fusion
time-series analysis

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Related Special Issue

Signal Processing Theory and Methods in Remote Sensing in Remote Sensing (12 articles)

Published Papers (11 papers)

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Research

32 pages, 14651 KiB

Open AccessArticle

An Adaptive Parameter Evolutionary Marine Predators Algorithm for Joint Resource Scheduling of Cooperative Jamming Networked Radar Systems

by Dejiang Lu, Siyi Cheng, You Chen, Xi Zhang, Haoyang Li and Tianjian Yang

Remote Sens. 2025, 17(8), 1325; https://doi.org/10.3390/rs17081325 - 8 Apr 2025

Viewed by 244

Abstract

This paper investigates the formation joint resource scheduling problem from the perspective of cooperative jamming against radar systems. First, the formation survivability is redefined based on the task requirements. Then, a hierarchical adaptive scheduling strategy solution framework is constructed for state prediction and detection fusion of the networked radar system. Considering the scene constraints, an Improved Adaptive Parameter Evolution Marine Predators Algorithm is designed as an optimizer and embedded in the proposed framework to jointly optimize the platform beam allocation and jamming mode selection. Based on the original algorithm, real number random coding is used to perform dimensional conversion of decision variables, an adaptive parameter evolution mechanism is designed to reduce the dependence on algorithm parameters, and an adaptive selection mechanism for dominant strategies and a search intensity control strategy are proposed to help decision-makers explore the optimal resource scheduling strategy quickly and accurately. Finally, considering the formation maneuvering behavior and incomplete information, the proposed method is compared with existing base strategies in different typical scenarios. It is proved that the proposed strategy can fully exploit the limited jamming resources and maximize the survivability of the formation in radar system cooperative jamming scenarios, demonstrating superior jamming performance and shorter decision time. Full article

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

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20 pages, 9834 KiB

Open AccessArticle

Nonlinear Seismic Signal Denoising Using Template Matching with Time Difference Detection Method

by Rongwei Xu, Bo Feng, Huazhong Wang, Chengliang Wu and Zhenbo Nie

Remote Sens. 2025, 17(4), 674; https://doi.org/10.3390/rs17040674 - 16 Feb 2025

Viewed by 476

Abstract

As seismic exploration shifts towards areas with more complex surface and subsurface structures, the complexity of the geological conditions often results in seismic data with low signal-to-noise ratio. It is therefore essential to implement denoising in order to enhance the signal-to-noise ratio of the seismic data. At present, the prevailing denoising techniques are based on the assumption that the signal adheres to linear model. However, this assumption is frequently invalid in complex geological conditions. The main challenge lies in the fact that linear models, which are foundational to traditional signal processing, fail to capture the nonlinear components of seismic signals. The objective of this paper is to present a methodology for the detection of nonlinear signal structures, with a particular focus on nonlinear time differences. We propose a method for detecting nonlinear time differences based on template matching, wherein the seismic wavelet is treated as the template. Template matching, a fundamental pattern recognition technique, plays a key role in identifying nonlinear structures within signals. By employing a local signal as a template, the template matching technique can identify all the structure of the signal, thereby enabling the detection of nonlinear features. By employing template matching, the nonlinear time differences in the signal are identified and corrected, thus enabling the signal to align with the assumption of linearity. Subsequently, linear denoising methods are employed to effectively remove noise and enhance the signal-to-noise ratio. The results of numerical experiments demonstrate that the proposed template matching method is highly accurate in detecting nonlinear time differences. Furthermore, the method’s efficacy in removing random noise from real seismic data is evident, underscoring its superiority. Full article

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

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19 pages, 3764 KiB

Open AccessArticle

STMSF: Swin Transformer with Multi-Scale Fusion for Remote Sensing Scene Classification

by Yingtao Duan, Chao Song, Yifan Zhang, Puyu Cheng and Shaohui Mei

Remote Sens. 2025, 17(4), 668; https://doi.org/10.3390/rs17040668 - 16 Feb 2025

Cited by 1 | Viewed by 894

Abstract

Emerging vision transformers (ViTs) are more powerful in modeling long-range dependences of features than conventional deep convolution neural networks (CNNs). Thus, they outperform CNNs in several computer vision tasks. However, existing ViTs fail to encounter the multi-scale characteristics of ground objects with various spatial sizes when they are applied to remote sensing (RS) scene images. Therefore, in this paper, a Swin transformer with multi-scale fusion (STMSF) is proposed to alleviate such an issue. Specifically, a multi-scale feature fusion module is proposed, so that features of ground objects at different scales in the RS scene can be well considered by merging multi-scale features. Moreover, a spatial attention pyramid network (SAPN) is designed to enhance the context of coarse features extracted with the transformer and further improve the network’s representation ability of multi-scale features. Experimental results over three benchmark RS scene datasets demonstrate that the proposed network obviously outperforms several state-of-the-art CNN-based and transformer-based approaches. Full article

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

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20 pages, 3750 KiB

Open AccessArticle

An Automatic Modulation Recognition Algorithm Based on Time–Frequency Features and Deep Learning with Fading Channels

by Xiaoya Zuo, Yuan Yang, Rugui Yao, Ye Fan and Lu Li

Remote Sens. 2024, 16(23), 4550; https://doi.org/10.3390/rs16234550 - 4 Dec 2024

Cited by 2 | Viewed by 1456

Abstract

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%. Full article

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

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21 pages, 3319 KiB

Open AccessArticle

Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach

by Talaat Abdelfattah, Ali Maher, Ahmed Youssef and Peter F. Driessen

Remote Sens. 2024, 16(22), 4211; https://doi.org/10.3390/rs16224211 - 12 Nov 2024

Cited by 1 | Viewed by 1301

Abstract

Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-resolution measurements and target detection. However, these signals are often degraded by noise, significantly impacting their processing and interpretation. Traditional denoising methods, including wavelet-based techniques, have been extensively used to address this issue, yet they often fall short in terms of optimizing performance due to fixed parameter settings. This paper introduces an innovative approach by combining wavelet denoising with long short-term memory (LSTM) networks specifically tailored for LFM signals in radar systems. By generating a dataset of LFM signals at various signal-to-noise Ratios (SNR) to ensure diversity, we systematically identified the optimal wavelet parameters for each noisy instance. These parameters served as training labels for the proposed LSTM-based architecture, which learned to predict the most effective denoising parameters for a given noisy LFM signal. Our findings reveal a significant enhancement in denoising performance, attributed to the optimized wavelet parameters derived from the LSTM predictions. This advancement not only demonstrates a superior denoising capability but also suggests a substantial improvement in radar signal processing, potentially leading to more accurate and reliable radar detections and measurements. The implications of this paper extend beyond modern radar applications, offering a framework for integrating deep learning techniques with traditional signal processing methods to optimize performance across various noise-dominated domains. Full article

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

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15 pages, 14372 KiB

Open AccessArticle

Calibration of Dual-Polarised Antennas for Air-Coupled Ground Penetrating Radar Applications

by Samuel J. I. Forster, Anthony J. Peyton and Frank J. W. Podd

Remote Sens. 2024, 16(21), 4114; https://doi.org/10.3390/rs16214114 - 4 Nov 2024

Cited by 2 | Viewed by 1407

Abstract

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB). Full article

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

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25 pages, 5900 KiB

Open AccessArticle

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

by Jing Xiao, Hang Zhang, Zeqi Shao, Yikai Zheng and Wenrui Ding

Remote Sens. 2024, 16(19), 3696; https://doi.org/10.3390/rs16193696 - 4 Oct 2024

Cited by 1 | Viewed by 931

Abstract

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods. Full article

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

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26 pages, 27118 KiB

Open AccessArticle

A Denoising Method Based on DDPM for Radar Emitter Signal Intra-Pulse Modulation Classification

by Shibo Yuan, Peng Li, Xu Zhou, Yingchao Chen and Bin Wu

Remote Sens. 2024, 16(17), 3215; https://doi.org/10.3390/rs16173215 - 30 Aug 2024

Viewed by 1114

Abstract

Accurately classifying the intra-pulse modulations of radar emitter signals is important for radar systems and can provide necessary information for relevant military command strategy and decision making. As strong additional white Gaussian noise (AWGN) leads to a lower signal-to-noise ratio (SNR) of received signals, which results in a poor classification accuracy on the classification models based on deep neural networks (DNNs), in this paper, we propose an effective denoising method based on a denoising diffusion probabilistic model (DDPM) for increasing the quality of signals. Trained with denoised signals, classification models can classify samples denoised by our method with better accuracy. The experiments based on three DNN classification models using different modal input, with undenoised data, data denoised by the convolutional denoising auto-encoder (CDAE), and our method’s denoised data, are conducted with three different conditions. The extensive experimental results indicate that our proposed method could denoise samples with lower values of the SNR, and that it is more effective for increasing the accuracy of DNN classification models for radar emitter signal intra-pulse modulations, where the average accuracy is increased from around 3 to 22 percentage points based on three different conditions. Full article

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

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19 pages, 5550 KiB

Open AccessArticle

GNSS/LiDAR/IMU Fusion Odometry Based on Tightly-Coupled Nonlinear Observer in Orchard

by Na Sun, Quan Qiu, Tao Li, Mengfei Ru, Chao Ji, Qingchun Feng and Chunjiang Zhao

Remote Sens. 2024, 16(16), 2907; https://doi.org/10.3390/rs16162907 - 8 Aug 2024

Viewed by 2716

Abstract

High-repetitive features in unstructured environments and frequent signal loss of the Global Navigation Satellite System (GNSS) severely limits the development of autonomous robot localization in orchard settings. To address this issue, we propose a LiDAR-based odometry pipeline GLIO, inspired by KISS-ICP and DLIO. GLIO is based on a nonlinear observer with strong global convergence, effectively fusing sensor data from GNSS, IMU, and LiDAR. This approach allows for many potentially interfering and inaccessible relative and absolute measurements, ensuring accurate and robust 6-degree-of-freedom motion estimation in orchard environments. In this framework, GNSS measurements are treated as absolute observation constraints. These measurements are tightly coupled in the prior optimization and scan-to-map stage. During the scan-to-map stage, a novel point-to-point ICP registration with no parameter adjustment is introduced to enhance the point cloud alignment accuracy and improve the robustness of the nonlinear observer. Furthermore, a GNSS health check mechanism, based on the robot’s moving distance, is employed to filter reliable GNSS measurements to prevent odometry crashed by sensor failure. Extensive experiments using multiple public benchmarks and self-collected datasets demonstrate that our approach is comparable to state-of-the-art algorithms and exhibits superior localization capabilities in unstructured environments, achieving an absolute translation error of 0.068 m and an absolute rotation error of 0.856°. Full article

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

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27 pages, 11527 KiB

Open AccessArticle

Unpaired Remote Sensing Image Dehazing Using Enhanced Skip Attention-Based Generative Adversarial Networks with Rotation Invariance

by Yitong Zheng, Jia Su, Shun Zhang, Mingliang Tao and Ling Wang

Remote Sens. 2024, 16(15), 2707; https://doi.org/10.3390/rs16152707 - 24 Jul 2024

Cited by 1 | Viewed by 1176

Abstract

Remote sensing image dehazing aims to enhance the visibility of hazy images and improve the quality of remote sensing imagery, which is essential for various applications such as object detection and classification. However, the lack of paired data in remote sensing image dehazing enhances the applications of unpaired image-to-image translation methods. Nonetheless, the considerable parameter size of such methods often leads to prolonged training times and substantial resource consumption. In this work, we propose SPRGAN, a novel approach leveraging Enhanced Perlin Noise-Based Generative Adversarial Networks (GANs) with Rotation Invariance to address these challenges. Firstly, we introduce a Spatial-Spectrum Attention (SSA) mechanism with Skip-Attention (SKIPAT) to enhance the model’s ability to interpret and process spectral information in hazy images. Additionally, we have significantly reduced computational overhead to streamline processing. Secondly, our approach combines Perlin Noise Masks in pre-training to simulate real foggy conditions, thereby accelerating convergence and enhancing performance. Then, we introduce a Rotation Loss (RT Loss) to ensure the model’s ability to dehaze images from different angles uniformly, thus enhancing its robustness and adaptability to diverse scenarios. At last, experimental results demonstrate the effectiveness of SPRGAN in remote sensing image dehazing, achieving better performance compared to state-of-the-art methods. Full article

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

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21 pages, 4506 KiB

Open AccessArticle

SREDet: Semantic-Driven Rotational Feature Enhancement for Oriented Object Detection in Remote Sensing Images

by Zehao Zhang, Chenhan Wang, Huayu Zhang, Dacheng Qi, Qingyi Liu, Yufeng Wang and Wenrui Ding

Remote Sens. 2024, 16(13), 2317; https://doi.org/10.3390/rs16132317 - 25 Jun 2024

Cited by 2 | Viewed by 1502

Abstract

Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature extraction in OOD becomes more challenging due to the diversity of object orientations. In this paper, we propose a semantic-driven rotational feature enhancement method, termed SREDet, to fully leverage the joint semantic and spatial information of oriented objects in the remote sensing images. We first construct a multi-rotation feature pyramid network (MRFPN), which leverages a fusion of multi-angle and multiscale feature maps to enhance the capability to extract features from different orientations. Then, considering feature confusion and contamination caused by the dense arrangement of objects and background interference, we present a semantic-driven feature enhancement module (SFEM), which decouples features in the spatial domain to separately enhance the features of objects and weaken those of backgrounds. Furthermore, we introduce an error source evaluation metric for rotated object detection to further analyze detection errors and indicate the effectiveness of our method. Extensive experiments demonstrate that our SREDet method achieves superior performance on two commonly used remote sensing object detection datasets (i.e., DOTA and HRSC2016). Full article

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

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