Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3174624
Faisal Hossain
{"title":"Reimagining the Surface Water and Ocean Topography Mission as the “Landsat” of Surface Water [Perspective]","authors":"Faisal Hossain","doi":"10.1109/mgrs.2022.3174624","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3174624","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46571674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3145500
Sartajvir Singh, R. K. Tiwari, V. Sood, R. Kaur, Shivendu Prashar
A scatterometer, as an active microwave radar sensor, measures the return of radar waves in the form of a backscatter coefficient after reflection or scattering from Earth’s surface. The primary objective of the scatterometer is to record the surface-wind vector observations over the ocean for the study of the climate, monitoring, the forecasting of cyclones/hurricanes, and air–sea interactions. Since its first launch in 1978, many technical improvements have been made to the scatterometer due to its potential for all-weather global-level monitoring. The scatterometer has found many emerging applications in different scientific domains, such as cryosphere, hydrology, agriculture, and climate studies, with the continuous development of methods and models.
{"title":"The Legacy of Scatterometers: Review of applications and perspective","authors":"Sartajvir Singh, R. K. Tiwari, V. Sood, R. Kaur, Shivendu Prashar","doi":"10.1109/mgrs.2022.3145500","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3145500","url":null,"abstract":"A scatterometer, as an active microwave radar sensor, measures the return of radar waves in the form of a backscatter coefficient after reflection or scattering from Earth’s surface. The primary objective of the scatterometer is to record the surface-wind vector observations over the ocean for the study of the climate, monitoring, the forecasting of cyclones/hurricanes, and air–sea interactions. Since its first launch in 1978, many technical improvements have been made to the scatterometer due to its potential for all-weather global-level monitoring. The scatterometer has found many emerging applications in different scientific domains, such as cryosphere, hydrology, agriculture, and climate studies, with the continuous development of methods and models.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"39-65"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47554862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3160664
Yuri Álvarez López, M. García-Fernández, G. Álvarez-Narciandi, F. Las-Heras Andrés
Advances in unmanned aerial vehicle (UAV) technology have fostered its use in a wide range of areas, such as agriculture and forestry, surveillance and security, and infrastructure inspection. One of the advantages of UAVs is their ability to conduct remote inspection and sensing by placing different kinds of sensors on board them. In this sense, UAV-based ground-penetrating radar (GPR) systems are of particular interest as they bring together the advantages of UAVs and GPR, resulting in contactless subsurface sensing and imaging systems capable of performing a fast scanning of difficult-to-access scenarios. This contribution reviews the advances on UAV-based GPR systems, describing their architecture and subsystems. In particular, an analysis of different UAV-based GPR systems is presented, focusing on the technical solutions adopted in each case and the detection capabilities that have been achieved. Attention will be also given to the methodologies implemented to obtain 3D high-resolution images of the underground. Finally, the main challenges faced by these systems concerning further improvements of the scanning throughput and the detection accuracy will be discussed.
{"title":"Unmanned Aerial Vehicle-Based Ground-Penetrating Radar Systems: A review","authors":"Yuri Álvarez López, M. García-Fernández, G. Álvarez-Narciandi, F. Las-Heras Andrés","doi":"10.1109/mgrs.2022.3160664","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3160664","url":null,"abstract":"Advances in unmanned aerial vehicle (UAV) technology have fostered its use in a wide range of areas, such as agriculture and forestry, surveillance and security, and infrastructure inspection. One of the advantages of UAVs is their ability to conduct remote inspection and sensing by placing different kinds of sensors on board them. In this sense, UAV-based ground-penetrating radar (GPR) systems are of particular interest as they bring together the advantages of UAVs and GPR, resulting in contactless subsurface sensing and imaging systems capable of performing a fast scanning of difficult-to-access scenarios. This contribution reviews the advances on UAV-based GPR systems, describing their architecture and subsystems. In particular, an analysis of different UAV-based GPR systems is presented, focusing on the technical solutions adopted in each case and the detection capabilities that have been achieved. Attention will be also given to the methodologies implemented to obtain 3D high-resolution images of the underground. Finally, the main challenges faced by these systems concerning further improvements of the scanning throughput and the detection accuracy will be discussed.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"66-86"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43643297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2021.3122248
San Jiang, Wanshou Jiang, Lizhe Wang
Three-dimensional mapping is an increasingly important feature for recent photogrammetry and remote sensing (RS) systems. Currently, unmanned aerial vehicles (UAVs) have become one of the extensively used RS platforms due to their high timeliness and flexibility on data acquisition as well as their high spatial resolution of recorded images. UAV-based 3D mapping has overwhelming advantages over traditional data sources from satellite and aerial platforms. Generally, the workflow of UAV-based 3D mapping consists of four major steps, including 1) data acquisition by using an optimal trajectory configuration, 2) image matching to obtain reliable correspondences, 3) aerial triangulation (AT) to resume accurate camera poses, and 4) dense image matching to generate point clouds with high density. The performance of the algorithms used in each step determines the reliability and precision of the final 3D mapping products.
{"title":"Unmanned Aerial Vehicle-Based Photogrammetric 3D Mapping: A survey of techniques, applications, and challenges","authors":"San Jiang, Wanshou Jiang, Lizhe Wang","doi":"10.1109/mgrs.2021.3122248","DOIUrl":"https://doi.org/10.1109/mgrs.2021.3122248","url":null,"abstract":"Three-dimensional mapping is an increasingly important feature for recent photogrammetry and remote sensing (RS) systems. Currently, unmanned aerial vehicles (UAVs) have become one of the extensively used RS platforms due to their high timeliness and flexibility on data acquisition as well as their high spatial resolution of recorded images. UAV-based 3D mapping has overwhelming advantages over traditional data sources from satellite and aerial platforms. Generally, the workflow of UAV-based 3D mapping consists of four major steps, including 1) data acquisition by using an optimal trajectory configuration, 2) image matching to obtain reliable correspondences, 3) aerial triangulation (AT) to resume accurate camera poses, and 4) dense image matching to generate point clouds with high density. The performance of the algorithms used in each step determines the reliability and precision of the final 3D mapping products.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"135-171"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43163388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3145478
Gabriele Cavallaro, Dora B. Heras, Zebin Wu, M. Maskey, S. López, P. Gawron, Mihai Coca, M. Datcu
The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.
{"title":"High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]","authors":"Gabriele Cavallaro, Dora B. Heras, Zebin Wu, M. Maskey, S. López, P. Gawron, Mihai Coca, M. Datcu","doi":"10.1109/mgrs.2022.3145478","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3145478","url":null,"abstract":"The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"329-345"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45477319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3165967
Peng Liu, Jun Yu Li, Lizhe Wang, G. He
In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative adversarial networks (GANs), as an important branch of deep learning, show promising performances in a variety of RS image fusions. This review provides an introduction to GANs for RS data fusion. We briefly review the frequently used architecture and characteristics of GANs in data fusion and comprehensively discuss how to use GANs to realize fusion for homogeneous RS, heterogeneous RS, and RS and ground observation (GO) data. We also analyze some typical applications with GAN-based RS image fusion. This review provides insight into how to make GANs adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss promising future research directions and make a prediction on their trends.
{"title":"Remote Sensing Data Fusion With Generative Adversarial Networks: State-of-the-art methods and future research directions","authors":"Peng Liu, Jun Yu Li, Lizhe Wang, G. He","doi":"10.1109/mgrs.2022.3165967","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3165967","url":null,"abstract":"In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative adversarial networks (GANs), as an important branch of deep learning, show promising performances in a variety of RS image fusions. This review provides an introduction to GANs for RS data fusion. We briefly review the frequently used architecture and characteristics of GANs in data fusion and comprehensively discuss how to use GANs to realize fusion for homogeneous RS, heterogeneous RS, and RS and ground observation (GO) data. We also analyze some typical applications with GAN-based RS image fusion. This review provides insight into how to make GANs adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss promising future research directions and make a prediction on their trends.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"295-328"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46452767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3145854
Lefei Zhang, Liangpei Zhang
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.
{"title":"Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities","authors":"Lefei Zhang, Liangpei Zhang","doi":"10.1109/mgrs.2022.3145854","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3145854","url":null,"abstract":"Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"270-294"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42278640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the fields of image restoration and image fusion, model- and data-driven methods are the two representative frameworks. However, both approaches have their respective advantages and disadvantages. Model-driven techniques consider the imaging mechanism, which is deterministic and theoretically reasonable; however, they cannot easily model complicated nonlinear problems. Data-driven schemes have a stronger prior-knowledge learning capability for huge data, especially for nonlinear statistical features; however, the interpretability of the networks is poor, and they are overdependent on training data. In this article, we systematically investigate the coupling of model- and data-driven methods, which has rarely been considered in the remote sensing image restoration and fusion communities. We are the first to summarize the coupling approaches into the following three categories: 1) data- and model-driven cascading methods, 2) variational models with embedded learning, and 3) model-constrained network learning methods. The typical existing and potential coupling techniques for remote sensing image restoration and fusion are introduced with application examples. This article also gives some new insights into potential future directions, in terms of both methods and applications.
{"title":"Coupling Model- and Data-Driven Methods for Remote Sensing Image Restoration and Fusion: Improving physical interpretability","authors":"Huanfeng Shen, Menghui Jiang, Jie Li, Chen Zhou, Q. Yuan, Liangpei Zhang","doi":"10.1109/mgrs.2021.3135954","DOIUrl":"https://doi.org/10.1109/mgrs.2021.3135954","url":null,"abstract":"In the fields of image restoration and image fusion, model- and data-driven methods are the two representative frameworks. However, both approaches have their respective advantages and disadvantages. Model-driven techniques consider the imaging mechanism, which is deterministic and theoretically reasonable; however, they cannot easily model complicated nonlinear problems. Data-driven schemes have a stronger prior-knowledge learning capability for huge data, especially for nonlinear statistical features; however, the interpretability of the networks is poor, and they are overdependent on training data. In this article, we systematically investigate the coupling of model- and data-driven methods, which has rarely been considered in the remote sensing image restoration and fusion communities. We are the first to summarize the coupling approaches into the following three categories: 1) data- and model-driven cascading methods, 2) variational models with embedded learning, and 3) model-constrained network learning methods. The typical existing and potential coupling techniques for remote sensing image restoration and fusion are introduced with application examples. This article also gives some new insights into potential future directions, in terms of both methods and applications.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"231-249"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45786139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-01DOI: 10.1109/mgrs.2022.3145502
Mingjing Zhao, Wei Li, Lu Li, Jinyue Hu, Pengge Ma, Ran Tao
Compared with radar and visible light imaging, infrared imaging has its own unique advantages, and in recent years, it has become a topic of intense research interest. Robust small-target detection is one of the key techniques in infrared search and tracking (IRST) applications, and there is no doubt that it has become an investigatory hot spot. In real applications, targets and backgrounds usually change quickly with very high velocities. In addition, a rapidly moving sensor platform typically makes the motion traces of the targets inconsistent. These factors reduce the detection performance of spatiotemporal-based methods, and thus single-frame infrared small-target detection is even more essential. In this survey, existing single-frame infrared small-target detection methods are comprehensively reviewed.
{"title":"Single-Frame Infrared Small-Target Detection: A survey","authors":"Mingjing Zhao, Wei Li, Lu Li, Jinyue Hu, Pengge Ma, Ran Tao","doi":"10.1109/mgrs.2022.3145502","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3145502","url":null,"abstract":"Compared with radar and visible light imaging, infrared imaging has its own unique advantages, and in recent years, it has become a topic of intense research interest. Robust small-target detection is one of the key techniques in infrared search and tracking (IRST) applications, and there is no doubt that it has become an investigatory hot spot. In real applications, targets and backgrounds usually change quickly with very high velocities. In addition, a rapidly moving sensor platform typically makes the motion traces of the targets inconsistent. These factors reduce the detection performance of spatiotemporal-based methods, and thus single-frame infrared small-target detection is even more essential. In this survey, existing single-frame infrared small-target detection methods are comprehensively reviewed.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"87-119"},"PeriodicalIF":14.6,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47053559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：