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19 pages, 9033 KiB

Open AccessArticle

UAV Network Path Planning and Optimization Using a Vehicle Routing Model

by Xiaotong Chen, Qin Li, Ronghao Li, Xiangyuan Cai, Jiangnan Wei and Hongying Zhao

Remote Sens. 2023, 15(9), 2227; https://doi.org/10.3390/rs15092227 - 22 Apr 2023

Cited by 4 | Viewed by 1969

Abstract

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for [...] Read more.

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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30 pages, 11053 KiB

Open AccessArticle

A Real-Time Incremental Video Mosaic Framework for UAV Remote Sensing

by Ronghao Li, Pengqi Gao, Xiangyuan Cai, Xiaotong Chen, Jiangnan Wei, Yinqian Cheng and Hongying Zhao

Remote Sens. 2023, 15(8), 2127; https://doi.org/10.3390/rs15082127 - 18 Apr 2023

Cited by 4 | Viewed by 1722

Abstract

Unmanned aerial vehicles (UAVs) are becoming increasingly popular in various fields such as agriculture, forest protection, resource exploration, and so on, due to their ability to capture high-resolution images quickly and efficiently at low altitudes. However, real-time image mosaicking of UAV image sequences, [...] Read more.

Unmanned aerial vehicles (UAVs) are becoming increasingly popular in various fields such as agriculture, forest protection, resource exploration, and so on, due to their ability to capture high-resolution images quickly and efficiently at low altitudes. However, real-time image mosaicking of UAV image sequences, especially during long multi-strip flights, remains challenging. In this paper, a real-time incremental UAV image mosaicking framework is proposed, which only uses the UAV image sequence, and does not rely on global positioning system (GPS), ground control points (CGPs), or other auxiliary information. Our framework aims to reduce spatial distortion, increase the speed of the operation in the mosaicking process, and output high-quality panorama. To achieve this goal, we employ several strategies. First, the framework estimates the approximate position of each newly added frame and selects keyframes to improve efficiency. Then, the matching relationship between keyframes and other frames is obtained by using the estimated position. After that, a new optimization method based on minimizing weighted reprojection errors is adopted to carry out precise position calculation of the current frame, so as to reduce the deformation caused by cumulative errors. Finally, the weighted partition fusion method based on the Laplacian pyramid is used to fuse and update the local image in real time to achieve the best mosaic result. We have carried out a series of experiments which show that our system can output high-quality panorama in real time. The proposed keyframe selection strategy and local optimization strategy can minimize cumulative errors, the image fusion strategy is highly robust, and it can effectively improve the panorama quality. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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18 pages, 7751 KiB

Open AccessArticle

Are Indices of Polarimetric Purity Excellent Metrics for Object Identification in Scattering Media?

by Xiaobo Li, Liping Zhang, Pengfei Qi, Zhiwei Zhu, Jianuo Xu, Tiegen Liu, Jingsheng Zhai and Haofeng Hu

Remote Sens. 2022, 14(17), 4148; https://doi.org/10.3390/rs14174148 - 24 Aug 2022

Cited by 14 | Viewed by 1685

Abstract

Polarization characteristics are significantly crucial for tasks in various fields, including the remote sensing of oceans and atmosphere, as well as the polarization LIDAR and polarimetric imaging in scattering media. Many polarimetric metrics (such as the degree of polarization, polarization angle diattenuation, and [...] Read more.

Polarization characteristics are significantly crucial for tasks in various fields, including the remote sensing of oceans and atmosphere, as well as the polarization LIDAR and polarimetric imaging in scattering media. Many polarimetric metrics (such as the degree of polarization, polarization angle diattenuation, and depolarization) have been proposed to enrich the characterization and improve the task performance in scattering media; yet, their related efficacy is limited, especially in high turbidity conditions. The indices of polarimetric purity (IPPs), including three different depolarization metrics, have been successfully applied to biomedical diagnosis. However, it is still debatable whether IPPs also are excellent metrics for identifying or distinguishing objects in scattering media. In this work, we seek to provide physical insights into the application of distinguishing and identifying different objects via IPPs. Imaging experiments are devised and performed on different objects, e.g., metals and plastics, under different turbidity levels, demonstrating the superiority of IPPs as excellent metrics for object identification in scattering conditions. The experimental results show that the IPPs images can enhance image contrast and improve discriminability, as well as break the limitation of traditional intensity-model imaging techniques when further combined with dehazing or enhancing algorithms. Importantly, as the used Mueller matrix (MM) and the related IPPs can also be obtained via other types of MM polarimeters (e.g., PolSAR and MM microscopy), the proposed solution and idea have potential for such applications as biomedical imaging, photogrammetry, and remote sensing. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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19 pages, 8045 KiB

Open AccessArticle

Polarized Intensity Ratio Constraint Demosaicing for the Division of a Focal-Plane Polarimetric Image

by Lei Yan, Kaiwen Jiang, Yi Lin, Hongying Zhao, Ruihua Zhang and Fangang Zeng

Remote Sens. 2022, 14(14), 3268; https://doi.org/10.3390/rs14143268 - 7 Jul 2022

Cited by 1 | Viewed by 1571

Abstract

Polarization is an independent dimension of light wave information that has broad application prospects in machine vision and remote sensing tasks. Polarization imaging using a division-of-focal-plane (DoFP) polarimetric sensor can meet lightweight and real-time application requirements. Similar to Bayer filter-based color imaging, demosaicing [...] Read more.

Polarization is an independent dimension of light wave information that has broad application prospects in machine vision and remote sensing tasks. Polarization imaging using a division-of-focal-plane (DoFP) polarimetric sensor can meet lightweight and real-time application requirements. Similar to Bayer filter-based color imaging, demosaicing is a basic and important processing step in DoFP polarization imaging. Due to the differences in the physical properties of polarization and the color of light waves, the widely studied color demosaicing method cannot be directly applied to polarization demosaicing. We propose a polarized intensity ratio constraint demosaicing model to efficiently account for the characteristics of polarization detection in this work. First, we discuss the special constraint relationship between the polarization channels. It can be simply described as: for a beam of light, the sum of the intensities detected by any two vertical ideal analyzers should be equal to the total light intensity. Then, based on this constraint relationship and drawing on the concept of guided filtering, a new polarization demosaicing method is developed. A method to directly use raw images captured by the DoFP detector as the ground truth for comparison experiments is then constructed to aid in the convenient collection of experimental data and extensive image scenarios. Results of both qualitative and quantitative experiments illustrate that our method is an effective and practical method to faithfully recover the full polarization information of each pixel from a single mosaic input image. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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18 pages, 2355 KiB

Open AccessArticle

Exploring the Potential of Optical Polarization Remote Sensing for Oil Spill Detection: A Case Study of Deepwater Horizon

by Zihan Zhang, Lei Yan, Xingwei Jiang, Jing Ding, Feizhou Zhang, Kaiwen Jiang and Ke Shang

Remote Sens. 2022, 14(10), 2398; https://doi.org/10.3390/rs14102398 - 17 May 2022

Cited by 7 | Viewed by 2305

Abstract

Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used [...] Read more.

Oil spills lead to catastrophic problems. In most oil spill cases, the spatial and temporal intractability of the detriment cannot be neglected, and problems related to economic, social and environmental factors constantly appear for a long time. Remote sensing has been widely used as a powerful means to conduct oil spill detection. Optical polarization remote sensing, thriving in recent years, shows a novel potential for oil spill detection. This paper provides a demonstration of the use of open-source POLDER/PARASOL polarization time-series data to detect oil spill. The Deepwater Horizon oil spill, one of the largest oil spill disasters, is utilized to explore the potential of optical polarization remote sensing for oil spill detection. A total of 24 feature combinations are organized to quantitatively study the positive effect of adding polarization information and the appropriate way to describe polarization characteristics. Random forest classifier models are trained with different combinations, and the results are assessed by 10-fold cross-validation. The improvement from adding polarization characteristics is remarkable ((average) accuracy: +0.51%; recall: +2.83%; precision: +3.49%; F₁ score: +3.01%, (maximum) accuracy: +0.80%; recall: +5.09%; precision: +6.92%; F₁ score: +4.72%), and coupling between the degree of polarization and the phase angle of polarization provides the best description of polarization information. This study confirms the potential of optical polarization remote sensing for oil spill detection, and some detailed problems related to model establishment and polarization feature characterization are discussed for the further application of polarization information. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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19 pages, 12009 KiB

Open AccessArticle

Temporal and Spatial Characteristics of the Global Skylight Polarization Vector Field

by Lei Yan, Yanfei Li, Wei Chen, Yi Lin, Feizhou Zhang, Taixia Wu, Jouni Peltoniemi, Hongying Zhao, Siyuan Liu and Zihan Zhang

Remote Sens. 2022, 14(9), 2193; https://doi.org/10.3390/rs14092193 - 4 May 2022

Cited by 5 | Viewed by 2405

Abstract

There are two widely recognized global fields in nature: the gravity field and the geomagnetic field. Universal gravitation and Earth rotation are important sources of the Earth’s gravity and geomagnetic fields, which are well known to us. After years of long-term observation, global [...] Read more.

There are two widely recognized global fields in nature: the gravity field and the geomagnetic field. Universal gravitation and Earth rotation are important sources of the Earth’s gravity and geomagnetic fields, which are well known to us. After years of long-term observation, global research, and analysis, it was discovered that we have neglected a direct incident energy of the universe on the Earth. Solar radiation, leading to energy exchange from the atmosphere 100 km above the land surface, is the energy source of the Earth. Polarization is one of the four basic physical properties of solar radiation. After the solar radiation reaches the surface of these media, it reflects, scatters or refracts, and exhibits different degrees of polarization. The polarized solar light forms the Earth–sky polarization vector field. The polarized light dispersion is expected to become a new method for global analysis of the human environment. Polarization detection is the best way to accurately explore the atmospheric effects. Local polarized skylight distribution was found in different sites in the world; however, the global distribution of the polarized sunlight radiation has never been explored. In this paper, we investigate the Global Skylight Polarization Field. This study aimed at providing new insight into the laws of polarization over our Earth. We use a Rayleigh scattering model to obtain the simulation results of the sky polarization field. Rayleigh scattering occurs when the particle size is much smaller than the wavelength of the incident electromagnetic wave. We also use a polarized fisheye camera to collect the sky polarization image and calculate the distribution pattern of the DOLP (degree of linear polarization) and AOLP (azimuth of linear polarization) of the skylight. The stability and gradual change in the degree of polarization in the zenith direction are verified, and the distribution law and daily change law of the degree of polarization in the sky are obtained. With the increase in the solar altitude angle, the degree of polarization will decrease. We also observed the skylight polarization in different weather conditions. Our results demonstrate the physical basis, characteristics, and usability of the polarization field. They show an inevitable trend from optical remote sensing to polarization remote sensing. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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17 pages, 3294 KiB

Open AccessArticle

Polarization Aberrations in High-Numerical-Aperture Lens Systems and Their Effects on Vectorial-Information Sensing

by Yuanxing Shen, Binguo Chen, Chao He, Honghui He, Jun Guo, Jian Wu, Daniel S. Elson and Hui Ma

Remote Sens. 2022, 14(8), 1932; https://doi.org/10.3390/rs14081932 - 16 Apr 2022

Cited by 13 | Viewed by 3183

Abstract

The importance of polarization aberrations has been recognized and studied in numerous optical systems and related applications. It is known that polarization aberrations are particularly crucial in certain photogrammetry and microscopy techniques that are related to vectorial information—such as polarization imaging, stimulated emission [...] Read more.

The importance of polarization aberrations has been recognized and studied in numerous optical systems and related applications. It is known that polarization aberrations are particularly crucial in certain photogrammetry and microscopy techniques that are related to vectorial information—such as polarization imaging, stimulated emission depletion microscopy, and structured illumination microscopy. Hence, a reduction in polarization aberrations would be beneficial to different types of optical imaging/sensing techniques with enhanced vectorial information. In this work, we first analyzed the intrinsic polarization aberrations induced by a high-NA lens theoretically and experimentally. The aberrations of depolarization, diattenuation, and linear retardance were studied in detail using the Mueller matrix polar-decomposition method. Based on an analysis of the results, we proposed strategies to compensate the polarization aberrations induced by high-NA lenses for hardware-based solutions. The preliminary imaging results obtained using a Mueller matrix polarimeter equipped with multiple coated aspheric lenses for polarization-aberration reduction confirmed that the conclusions and strategies proposed in this study had the potential to provide more precise polarization information of the targets for applications spanning across classical optics, remote sensing, biomedical imaging, photogrammetry, and vectorial optical-information extraction. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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Review

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42 pages, 38908 KiB

Open AccessReview

Polarimetric Imaging via Deep Learning: A Review

by Xiaobo Li, Lei Yan, Pengfei Qi, Liping Zhang, François Goudail, Tiegen Liu, Jingsheng Zhai and Haofeng Hu

Remote Sens. 2023, 15(6), 1540; https://doi.org/10.3390/rs15061540 - 11 Mar 2023

Cited by 19 | Viewed by 4514

Abstract

Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid [...] Read more.

Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field. Full article

(This article belongs to the Special Issue Advanced Light Vector Field Remote Sensing)

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