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Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery
published in Remote Sensing; doi:10.3390/rs12071097 Abstract This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of...
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Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery
published in Remote Sensing; doi:10.3390/rs12071097 Abstract This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of...
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Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
publised in The Cryosphere doi:10.5194/tc-14-1083-2020 Abstract Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with...
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Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
published in ISPRS Journal of Photogrammetry and Remote Sensing doi:10.1016/j.isprsjprs.2019.09.009 Abstract The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by...
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Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network
publised in Remote Sensing; doi:10.3390/rs11151741 Abstract Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is a good indicator of severe weather phenomena...
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A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data
published in Remote Sensing doi:10.3390/rs11121454 Abstract This paper proposes a complete framework of a machine learning-based model that detects convective initiation (CI) from geostationary meteorological satellite data. The suggested framework consists of three main processes: (1) An automated sampling tool; (2) machine learning-based CI detection modelling; (3) repeated model tuning...
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The Estimation of Arctic Air Temperature in Summer Based on Machine Learning Approaches Using IABP Buoy and AMSR2 Satellite Data
published in Korean Journal of Remote Sensing doi:10.7780/kjrs.2018.34.6.2.10 Abstract It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited...
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Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery
***published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [doi:10.1109/JSTARS.2018.2880783](http://dx.doi.org/10.1109/JSTARS.2018.2880783)*** ### Abstract Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even...
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Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery
published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing doi:10.1109/JSTARS.2018.2880783 Abstract Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even better...
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Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery
***published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [doi:10.1109/JSTARS.2018.2880783](http://dx.doi.org/10.1109/JSTARS.2018.2880783)*** ### Abstract Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even...