Object Tracking Based on Satellite Videos: A Literature Review

Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances

Remote sensing object detection (RSOD), one of the most fundamental and challenging tasks in the remote sensing field, has received longstanding attention. In recent years, deep learning techniques have demonstrated robust feature representation capabilities and led to a big leap in the development of RSOD techniques. In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. More than 300 papers are covered in this review. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision, and systematically review the corresponding methods developed in a hierarchical division manner. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD. Future research directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than 300 papers relevant to the RSOD filed were reviewed in this surve

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Assessing and enhancing migration of human myogenic progenitors using directed iPS cell differentiation and advanced tissue modelling

Muscle satellite stem cells (MuSCs) are responsible for skeletal muscle growth and regeneration. Despite their differentiation potential, human MuSCs have limited in vitro expansion and in vivo migration capacity, limiting their use in cell therapies for diseases affecting multiple skeletal muscles. Several protocols have been developed to derive MuSC-like progenitors from human induced pluripotent stem (iPS) cells (hiPSCs) to establish a source of myogenic cells with controllable proliferation and differentiation. However, current hiPSC myogenic derivatives also suffer from limitations of cell migration, ultimately delaying their clinical translation. Here we use a multi-disciplinary approach including bioinformatics and tissue engineering to show that DLL4 and PDGF-BB improve migration of hiPSC-derived myogenic progenitors. Transcriptomic analyses demonstrate that this property is conserved across species and multiple hiPSC lines, consistent with results from single cell motility profiling. Treated cells showed enhanced trans-endothelial migration in transwell assays. Finally, increased motility was detected in a novel humanised assay to study cell migration using 3D artificial muscles, harnessing advanced tissue modelling to move hiPSCs closer to future muscle gene and cell therapies

Studies of net community productivity in a near-coastal temperate ecosystem

Understanding the biological contribution to the carbon cycle is important to accurately calculate oceanic carbon budgets. The biological contribution to air-sea flux can be expressed as net community productivity (NCP), or the difference between gross primary production and community respiration. This study conducted two experiments to constrain NCP in a near-coastal region. The first experiment conducted in the western Gulf of Maine (GoM) sought to identify an indirect optical proxy for NCP that would allow for the determination of NCP remotely by satellite in the future. NCP results indicated that the GoM was near equilibrium during our study. Changes in particulate organic carbon inventory derived from beam attenuation proved to be the most robust proxy of NCP. The second experiment evaluated a novel custom-built autonomous incubation instrument for continuous NCP and respiration measurement in the Piscataqua Estuary Inlet. Although some questionable data patterns were occasionally observed, NCP and respiration rates correlated well with the literature where good data was recorded

Decoupling of net community and export production on submesoscales in the Sargasso Sea

Author Posting. © American Geophysical Union, 2015. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 29 (2015): 1266–1282, doi:10.1002/2014GB004913.Determinations of the net community production (NCP) in the upper ocean and the particle export production (EP) should balance over long time and large spatial scales. However, recent modeling studies suggest that a horizontal decoupling of flux-regulating processes on submesoscales (≤10 km) could lead to imbalances between individual determinations of NCP and EP. Here we sampled mixed-layer biogeochemical parameters and proxies for NCP and EP during 10, high-spatial resolution (~2 km) surface transects across strong physical gradients in the Sargasso Sea. We observed strong biogeochemical and carbon flux variability in nearly all transects. Spatial coherence among measured biogeochemical parameters within transects was common but rarely did the same parameters covary consistently across transects. Spatial variability was greater in parameters associated with higher trophic levels, such as chlorophyll in >5.0 µm particles, and variability in EP exceeded that of NCP in nearly all cases. Within sampling transects, coincident EP and NCP determinations were uncorrelated. However, when averaged over each transect (30 to 40 km in length), we found NCP and EP to be significantly and positively correlated (R = 0.72, p = 0.04). Transect-averaged EP determinations were slightly smaller than similar NCP values (Type-II regression slope of 0.93, standard deviation = 0.32) but not significantly different from a 1:1 relationship. The results show the importance of appropriate sampling scales when deriving carbon flux budgets from upper ocean observations.NASA Ocean Carbon and Biogeochemistry program Grant Number: NNX11AL94G; WHOI Postdoctoral Scholar fellowship; NASA ACE Grant Number: NNX12AJ25G; NSF Grant Number: OCE-07523662016-02-2

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Decoupling of Net Community Production and Export Production at Submesoscale Fronts in the Sargasso Sea

Determinations of the net community production (NCP) in the upper ocean and the particle export production (EP) should balance over long time and large spatial scales. However, recent modeling studies suggest that a horizontal decoupling of flux-regulating processes on submesoscales (≤10 km) could lead to imbalances between individual determinations of NCP and EP. Here we sampled mixed-layer biogeochemical parameters and proxies for NCP and EP during 10, high-spatial resolution (~2 km) surface transects across strong physical gradients in the Sargasso Sea. We observed strong biogeochemical and carbon flux variability in nearly all transects. Spatial coherence among measured biogeochemical parameters within transects was common but rarely did the same parameters covary consistently across transects. Spatial variability was greater in parameters associated with higher trophic levels, such as chlorophyll in \u3e5.0 µm particles, and variability in EP exceeded that of NCP in nearly all cases. Within sampling transects, coincident EP and NCP determinations were uncorrelated. However, when averaged over each transect (30 to 40 km in length), we found NCP and EP to be significantly and positively correlated (R = 0.72, p = 0.04). Transect-averaged EP determinations were slightly smaller than similar NCP values (Type-II regression slope of 0.93, standard deviation = 0.32) but not significantly different from a 1:1 relationship. The results show the importance of appropriate sampling scales when deriving carbon flux budgets from upper ocean observations