Performance evaluation of artificial neural networks for natural terrain classification

Akwensi, Perpetual Hope; Brantson, Eric Thompson; Niipele, Johanna Ngula; Ziggah, Yao Yevenyo

doi:10.1007/s12518-021-00360-9

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the neurons that receive input information are then transformed using an activation function to ensure nonlinear prediction [26]. This study used the sigmoid function as the activation function [65]. Alternatively, C4.5 does not require any parameter setting due to its inherent simplicity.…”

Section: B Machine Learning Parametersmentioning

confidence: 99%

An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Accurate classifications of land use/land cover (LULC) in arid regions are vital for analyzing changes in climate.We propose an ensemble learning approach for improving LULC classification accuracy in Xinjiang, northwest China. First, multisource geographical datasets were applied, and the study area was divided into Northern Xinjiang, Tianshan, and Southern Xinjiang. Second, five machine learning algorithms-k-nearest neighbor (KNN), Support Vector Machine (SVM), random forest (RF), artificial neural network (ANN), and C4.5-were chosen to develop different ensemble learning strategies according to the climatic and topographic characteristics of each sub-region. Third, stratified random sampling was used to obtain training samples and optimal parameters for each machine learning algorithm. Lastly, each derived approach was applied across Xinjiang, and sub-region performance was evaluated. The results showed that the LULC classification accuracy achieved across Xinjiang via the proposed ensemble learning approach was improved by ≥ 6.85% compared with individual machine learning algorithms. By specific sub-region, the accuracies for Northern Xinjiang, Tianshan, and Southern Xinjiang increased by ≥ 6.70%, 5.87%, and 6.86%, respectively. Moreover, the ensemble learning strategy combining four machine learning algorithms (i.e., SVM, RF, ANN, and C4.5) was superior across Xinjiang and Tianshan; whereas, the three-algorithm (i.e., SVM, RF, and ANN) strategy worked best for the Northern and Southern Xinjiang. The innovation of this study is to develop a novel ensemble learning approach to divide Xinjiang into different sub-regions, accurately classify land cover, and generate a new land cover product for simulating climate change in Xinjiang.

show abstract

Section: B Machine Learning Parametersmentioning

confidence: 99%

An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Moreover, there are different varieties of ANN; however, BPNN is the most widely applied variant of ANN in several disciplines; and hence is regarded as the basis for comparison with other ML techniques. Evidence of its efficiency has been documented in diverse geospatial disciplines, such as in geodetic coordinate transformation (Tierra et al., 2008; Ziggah et al., 2016), earth orientation (Schuh et al., 2002), natural terrain classification (Akwensi et al., 2021), surface energy balance (Alemohammad et al., 2017), mass movement (Mandal & Mondal, 2019) and bathymetry inversion (Annan & Wan, 2022).…”

Section: Introductionmentioning

confidence: 99%

Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network

2023

Self Cite

View full text Add to dashboard Cite

Altimetry satellites have played significant roles in Earth science, and one of their main applications is marine gravimetry. Apart from the marine geoid, the main geodetic end-product from altimetry satellites has been marine gravity anomaly. Several institutes and universities have published very highly accurate marine gravity anomaly products, such as: Andersen and Knudsen (2019), Sandwell et al. (2019), andAndersen et al. (2021). Indeed, altimetry satellite observations can also be used to derive gravity gradient products, which are very beneficial to

show abstract

Enhanced Landslide Spatial Prediction Using Hybrid Deep Learning Model and SHAP Analysis: A Case Study of the Tuyen Quang-Ha Giang Expressway, Vietnam

Nguyen,

Nguyen

et al. 2024

J Indian Soc Remote Sens

View full text Add to dashboard Cite

Performance evaluation of artificial neural networks for natural terrain classification

Cited by 5 publications

References 32 publications

An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

An Ensemble Learning Approach for Land Use/Land Cover Classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network

Enhanced Landslide Spatial Prediction Using Hybrid Deep Learning Model and SHAP Analysis: A Case Study of the Tuyen Quang-Ha Giang Expressway, Vietnam

Contact Info

Product

Resources

About