Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey

Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 14; no. 23; p. 6017
Main Authors: Savelonas, Michalis A., Veinidis, Christos N., Bartsokas, Theodoros K.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2022
Subjects:
Algorithms
Cameras
Cloud computing
Clustering
Computer applications
Computer vision
Data mining
Decision trees
Deep learning
Digital Elevation Models
Domains
Earth science
geoscience
Graphics processing units
Hyperspectral imaging
Labeling
Land cover
Lasers
LiDAR
Machine learning
multispectral imaging
Neural networks
Pattern analysis
Pattern recognition
Radar imaging
Remote sensing
Satellites
Sensors
Software
Standardization
Synthetic aperture radar
Target detection
Topography
Two dimensional analysis
Unmanned aerial vehicles
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience objects and processes, as well as non-stationarity in space and/or time, provide an ideal test bed for advanced computer vision techniques. On the other hand, the developments in pattern recognition, especially with the rapid evolution of powerful graphical processing units (GPUs) and the subsequent deep learning breakthrough, enable valuable computational tools, which can aid geoscientists in important problems, such as land cover mapping, target detection, pattern mining in imaging data, boundary extraction and change detection. In this landscape, classical computer vision approaches, such as active contours, superpixels, or descriptor-guided classification, provide alternatives that remain relevant when domain expert labelling of large sample collections is often not feasible. This issue persists, despite efforts for the standardization of geoscience datasets, such as Microsoft’s effort for AI on Earth, or Google Earth. This work covers developments in applications of computer vision and pattern recognition on geoscience-related imaging data, following both pre-deep learning and post-deep learning paradigms. Various imaging modalities are addressed, including: multispectral images, hyperspectral images (HSIs), synthetic aperture radar (SAR) images, point clouds obtained from light detection and ranging (LiDAR) sensors or digital elevation models (DEMs).
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs14236017