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Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (6 July 2024) | Viewed by 6958

Special Issue Editor

Dr. Armin Moghimi

E-Mail Website
Guest Editor
Ludwig-Franzius-Institute for Hydraulic, Estuarine and Coastal Engineering, Leibniz University Hannover, D-30167 Hannover, Germany
Interests: remote sensing; photogrammetry; registeration; classification; radiometric; normalization; radiometric correction; color consistency; random forest; iran; tehran; sentinel 1; sentinel 2; landsat 8; landsat 9; landsat; irs; uav; wetland; change detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancements in remote sensing, Synthetic Aperture Radar (SAR), and optical imaging technologies have sparked a revolution in how we observe and analyze the Earth's surface and beyond. This progress has opened new horizons and presented unparalleled opportunities for understanding our planet and addressing a myriad of challenges. In this Special Issue, we aim to highlight the cutting-edge techniques and multifaceted applications of these technologies. By harnessing the potential of remote sensing, SAR, and optical imaging, researchers have significantly enhanced their capabilities to explore the Earth's landscapes, monitor environmental changes, and support various sectors with valuable insights.

From SAR's ability to penetrate clouds and provide all-weather imaging to optical spaceborne sensors capturing high-resolution imagery, these technologies have enabled us to study land use patterns, assess natural disasters, analyze agricultural productivity, and map urban development. Additionally, the integration of airborne imaging systems has further expanded the scope of application, offering dynamic perspectives and precise data collection a range of scenarios.

We encourage contributions that is through topics that include, but are not limited to, the following:

  • Advances in remote sensing platforms, sensors, and imaging techniques.
  • Image processing techniques for remote sensing data, including noise removal and artifact correction.
  • Super-resolution imaging techniques for enhancing spatial details in remote sensing.
  • Fusion of SAR and optical remote sensing data for improved analysis and interpretation.
  • Applications of remote sensing in environmental monitoring, management, and climate change studies.
  • Land cover and land use classification using remote sensing data, incorporating deep learning approaches.
  • Object detection and recognition in SAR and optical images through deep learning techniques.
  • Semantic segmentation of remote sensing imagery using convolutional neural networks (CNN) and other advanced AI.
  • Change detection in SAR and optical time series data using deep learning methods.
  • Transfer learning and domain adaptation for improved analysis of remote sensing data.
  • Hyperspectral image classification and analysis using deep learning models.
  • Image fusion techniques for integrating SAR and optical data in remote sensing applications.
  • Unsupervised feature extraction methods using deep learning for remote sensing data analysis.
  • Deep learning-based 3D reconstruction and modeling using airborne and spaceborne imagery.
  • Radiometric and atmospheric correction techniques for accurate interpretation of remote sensing imagery.
  • Geometric correction and registration methods for precise alignment of remote sensing data.
  • Data compression and storage techniques for efficient management of remote sensing datasets.
  • Preprocessing methods for seamless image mosaicking and stitching of remote sensing data.
  • Quality assessment and validation of preprocessed remote sensing data.
  • Data interpolation and gap-filling techniques for handling incomplete remote sensing datasets.

Dr. Armin Moghimi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (5 papers)

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Research

20 pages, 3271 KiB  
Article
A Bilevel Programming Approach for Optimizing Multi-Satellite Collaborative Mission Planning
by Yi Wang and Desheng Liu
Sensors 2024, 24(19), 6242; https://doi.org/10.3390/s24196242 - 26 Sep 2024
Viewed by 582
Abstract
With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, [...] Read more.
With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency. Full article
(This article belongs to the Special Issue Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains)
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25 pages, 11350 KiB  
Article
Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models
by Kangtai Chang, Zhifang Zhao, Dingyi Zhou, Zhuyu Tian and Chang Wang
Sensors 2024, 24(15), 4770; https://doi.org/10.3390/s24154770 - 23 Jul 2024
Viewed by 814
Abstract
Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence [...] Read more.
Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east–west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities. Full article
(This article belongs to the Special Issue Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains)
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18 pages, 11901 KiB  
Article
LIRRN: Location-Independent Relative Radiometric Normalization of Bitemporal Remote-Sensing Images
by Armin Moghimi, Vahid Sadeghi, Amin Mohsenifar, Turgay Celik and Ali Mohammadzadeh
Sensors 2024, 24(7), 2272; https://doi.org/10.3390/s24072272 - 2 Apr 2024
Cited by 1 | Viewed by 1396
Abstract
Relative radiometric normalization (RRN) is a critical pre-processing step that enables accurate comparisons of multitemporal remote-sensing (RS) images through unsupervised change detection. Although existing RRN methods generally have promising results in most cases, their effectiveness depends on specific conditions, especially in scenarios with [...] Read more.
Relative radiometric normalization (RRN) is a critical pre-processing step that enables accurate comparisons of multitemporal remote-sensing (RS) images through unsupervised change detection. Although existing RRN methods generally have promising results in most cases, their effectiveness depends on specific conditions, especially in scenarios with land cover/land use (LULC) in image pairs in different locations. These methods often overlook these complexities, potentially introducing biases to RRN results, mainly because of the use of spatially aligned pseudo-invariant features (PIFs) for modeling. To address this, we introduce a location-independent RRN (LIRRN) method in this study that can automatically identify non-spatially matched PIFs based on brightness characteristics. Additionally, as a fast and coregistration-free model, LIRRN complements keypoint-based RRN for more accurate results in applications where coregistration is crucial. The LIRRN process starts with segmenting reference and subject images into dark, gray, and bright zones using the multi-Otsu threshold technique. PIFs are then efficiently extracted from each zone using nearest-distance-based image content matching without any spatial constraints. These PIFs construct a linear model during subject–image calibration on a band-by-band basis. The performance evaluation involved tests on five registered/unregistered bitemporal satellite images, comparing results from three conventional methods: histogram matching (HM), blockwise KAZE, and keypoint-based RRN algorithms. Experimental results consistently demonstrated LIRRN’s superior performance, particularly in handling unregistered datasets. LIRRN also exhibited faster execution times than blockwise KAZE and keypoint-based approaches while yielding results comparable to those of HM in estimating normalization coefficients. Combining LIRRN and keypoint-based RRN models resulted in even more accurate and reliable results, albeit with a slight lengthening of the computational time. To investigate and further develop LIRRN, its code, and some sample datasets are available at link in Data Availability Statement. Full article
(This article belongs to the Special Issue Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains)
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23 pages, 12385 KiB  
Article
SBAS-InSAR Based Deformation Monitoring of Tailings Dam: The Case Study of the Dexing Copper Mine No.4 Tailings Dam
by Weiguo Xie, Jianhua Wu, Hua Gao, Jiehong Chen and Yufeng He
Sensors 2023, 23(24), 9707; https://doi.org/10.3390/s23249707 - 8 Dec 2023
Cited by 2 | Viewed by 2062
Abstract
The No.4 tailings pond of the Dexing Copper Mine is the second largest in Asia. The tailing pond is a dangerous source of man-made debris flow with high potential energy. In view of the lack of effective and low-cost global safety monitoring means [...] Read more.
The No.4 tailings pond of the Dexing Copper Mine is the second largest in Asia. The tailing pond is a dangerous source of man-made debris flow with high potential energy. In view of the lack of effective and low-cost global safety monitoring means in this region, in this paper, the time-series InSAR technology is innovatively introduced to monitor the deformation of tailings dam and significant key findings are obtained. First, the surface deformation information of the tailings pond and its surrounding areas was extracted by using SBAS-InSAR technology and Sentinel-1A data. Second, the cause of deformation is explored by analyzing the deformation rate, deformation accumulation, and three typical deformation rate profiles of the representative observation points on the dam body. Finally, the power function model is used to predict the typical deformation observation points. The results of this paper indicated that: (1) the surface deformation of the tailings dam can be categorized into two directions: the upper portion of the dam moving away from the satellite along the Line of Sight (LOS) at a rate of −40 mm/yr, whereas the bottom portion approaching the satellite along the LOS at a rate of 8 mm/yr; (2) the deformation of the dam body is mainly affected by the inventory deposits and the construction materials of the dam body; (3) according to the current trend, deformation of two typical observation points in the LOS direction will reach the cumulative deformation of 80 mm and −360 mm respectively. The research results can provide data support for safety management of No.4 tailings dam in the Dexing Copper Mine, and provide a method reference for monitoring other similar tailings dams. Full article
(This article belongs to the Special Issue Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains)
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18 pages, 6133 KiB  
Article
Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
by Danhua Zhang and Hui Ni
Sensors 2023, 23(23), 9313; https://doi.org/10.3390/s23239313 - 21 Nov 2023
Cited by 2 | Viewed by 1361
Abstract
Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and [...] Read more.
Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (p < 0.05), and 21 factors were significantly correlated with forest biomass (p < 0.01). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion. Full article
(This article belongs to the Special Issue Techniques and Applications of Remote Sensing, Synthetic Aperture Radar (SAR), and Optical Imaging in Diverse Domains)
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