A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification

Suliman, Alaeldin; Zhang, Yun

doi:10.17265/2159-581x/2015.01.004

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…MLP is an ANN for learning patterns through backpropagation [39], [40], [41]. With layers of interconnected neurons and non-linear activation functions, MLP is versatile in approximating non-linear relationships (Baraldi et al, 2001;Suliman and Zhang, 2015). Hyperparameters contribute to model complexity, non-linear mapping, convergence speed, and preventing overfitting [42].…”

Section: A Machine Learningmentioning

confidence: 99%

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

Ranjbar,

Losos,

Hoffman

et al. 2024

Preprint

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This study investigates high-frequency mapping of downward shortwave radiation (DSR) at Earth's surface using the Advanced Baseline Imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite -R Series (GOES-R). The existing GOES-R DSR product (DSR ABI ) offers hourly temporal resolution and spatial resolution of 0.25 o . To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 Cloud and Moisture Imagery (CMI) product (five minutes) and its native spatial resolution of two-kilometer at nadir. We compared four common ML regression models through the Leave-One-Out Cross-Validation (LOOCV) algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that Gradient Boosting Regression (GBR) achieves the best performance (R² = 0.916, RMSE = 88.05 W m -2 ) with efficient computation compared to Long Short-Term Memory (LSTM), which exhibited similar performance. DSR estimates from the GBR model (DSR ALIVE ) outperform DSR ABI across various temporal resolutions and sky conditions. DSR ALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (five-minute intervals) with R² exceeding 0.85 and RMSE=122 W m -2 . We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.

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Section: A Machine Learningmentioning

confidence: 99%

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

Ranjbar,

Losos,

Hoffman

et al. 2024

Preprint

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“…Agronomy 2024, 14, 719 7 of 24 algorithm. Thus, the back propagation utilises the error function to adjust the weight of each input to neurons in order to gradually reduce the error, which is the difference between the actual ANN's and the sought output [97,98]. The activation function introduces non-linearity to the data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The learning process is made possible by the back propagation algorithm. Thus, the back propagation utilises the error function to adjust the weight of each input to neurons in order to gradually reduce the error, which is the difference between the actual ANN's and the sought output [97,98].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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“…The neural networks used in this chapter are sigmoidal neurons trained with backpropagation [17]. Other functioning details are given in [12,13].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Autonomous Robots and Behavior Initiators

Chang¹

2018

Human-Robot Interaction - Theory and Application

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We use an autonomous neural controller (ANC) that handles the mechanical behavior of virtual, multi-joint robots, with many moving parts and sensors distributed through the robot's body, satisfying basic Newtonian laws. As in living creatures, activities inside the robot include behavior initiators: self-activating networks that burn energy and function without external stimulus. Autonomy is achieved by mimicking the dynamics of biological brains, in resting situations, a default state network (DSN), specialized set of energy burning neurons, assumes control and keeps the robot in a safe condition, where other behaviors can be brought to use. Our ANC contains several kinds of neural nets trained with gradient descent to perform specialized jobs. The first group generates moving wave activities in the robot muscles, the second yields basic position/presence prediction information about sensors, the third acts as timing masters, empowering sequential tasks. We add a fourth category of self-activating networks that push behavior from the inside. Through evolutive methods, the composed network share clue information along a few connecting weights, producing self-motivated robots, capable of achieving noticeable self-level of competence. We show that this spirited robot interacts with humans and, through appropriate interfaces, learn complex behaviors that satisfy unknown, subjacent human expectative.

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A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification

Cited by 13 publications

References 11 publications

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

High-Frequency Mapping of Downward Shortwave Radiation from GOES-R Using Gradient Boosting

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Autonomous Robots and Behavior Initiators

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