Semantic segmentation of remote sensing data using Gaussian processes and higher-order CRFS

Piramanayagam, Sankaranarayanan; Monteiro, Sildomar T.; Saber, Eli

doi:10.1109/igarss.2017.8128238

Cited by 3 publications

(1 citation statement)

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“…As very high-resolution imaging becomes more accessible, complex scenes require different methods to classify images. A novel framework using semantic segmentation and higherorder conditional random fields (CRF) exploits the range of contextual information available with higher order CRF [6]. The author's approach uses a harmonic label co-existence (which is typical), but they also introduce local object co-occurence.…”

Section: Spatial Statisticsmentioning

confidence: 99%

Leveraging Gaussian Processes in Remote Sensing

Foley

2024

Preprint

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Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous. Remote sensing enables real time data monitoring and collection related to environmental and industrial processes, like the power grid system. Monitoring the power grid successfully involves diverse sources of data including that inherent to the power operation and ambient atmospheric weather data. Detection methods can identify anomalies like arcing, and climate data in particular is becoming increasingly important in order to maintain power supply. Gaussian processes (GPs) are a well-established Bayesian method for analyzing data. However, the computational complexity of GPs limits their scalability. This is a challenge when dealing with remote sensing datasets, where acquiring a significant amount of data is common. Alternatively, traditional machine learning methods perform quickly and accurately, but lack the generalizability innate to GPs. The focus of this review is burgeoning research that leverages Gaussian processes and machine learning in remote sensing applications.

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Section: Spatial Statisticsmentioning

confidence: 99%

Leveraging Gaussian Processes in Remote Sensing

Foley

2024

Preprint

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Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)

et al. 2019

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Leveraging Gaussian Processes in Remote Sensing

Foley

2024

Energies

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Power grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous. Monitoring the power grid involves diverse sources of data, including those inherent to the power operation (inertia, damping, etc.) and ambient atmospheric weather data. TheAutonomous Intelligence Measurements and Sensor Systems (AIMS) project at the Oak Ridge National Laboratory is a project to develop a machine-controlled response team capable of autonomous inspection and reporting with the explicit goal of improved grid reliability. Gaussian processes (GPs) are a well-established Bayesian method for analyzing data. GPs have been successful in satellite sensing for physical parameter estimation, and the use of drones for remote sensing is becoming increasingly common. However, the computational complexity of GPs limits their scalability. This is a challenge when dealing with remote sensing datasets, where acquiring large amounts of data is common. Alternatively, traditional machine learning methods perform quickly and accurately but lack the generalizability innate to GPs. The main objective of this review is to gather burgeoning research that leverages Gaussian processes and machine learning in remote sensing applications to assess the current state of the art. The contributions of these works show that GP methods achieve superior model performance in satellite and drone applications. However, more research using drone technology is necessary. Furthermore, there is not a clear consensus on which methods are the best for reducing computational complexity. This review paves several routes for further research as part of the AIMS project.

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Semantic segmentation of remote sensing data using Gaussian processes and higher-order CRFS

Cited by 3 publications

References 9 publications

Leveraging Gaussian Processes in Remote Sensing

Leveraging Gaussian Processes in Remote Sensing

Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)

Leveraging Gaussian Processes in Remote Sensing

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