SRTM DEM Correction Using Ensemble Machine Learning Algorithm

Ouyang, Zhangxian; Zhou, Cui; Xie, Jin; Zhu, Jianjun; Gui, Zhilun; Ao, Minsi

doi:10.3390/rs15163946

Cited by 10 publications

(3 citation statements)

References 61 publications

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“…Another strategy is to leverage machine learning algorithms for correcting SRTM DEM. For instance, integrating multiple machine learning algorithms can enhance the accuracy of SRTM DEMs [25]. In forested areas, one method for correcting the SRTM DEM involves subtracting the simulated forest height obtained through Random Forest (RF) from the original SRTM DEM [26].…”

Section: Introductionmentioning

confidence: 99%

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

Sun,

Zhou,

Xie

et al. 2023

Forests

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The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is extensively utilized in various fields, such as forestry, oceanography, geology, and hydrology. However, due to limitations in radar side-view imaging, the SRTM DEM still contains gaps and anomalies, particularly in areas with an intricate topography, like forests. To enhance the accuracy of the SRTM DEM in forested regions, commonly employed approaches include regression analysis and artificial neural networks (ANN). Nevertheless, existing regression methods struggle to accurately capture the intricate nonlinear relationship between the error and influencing factors. Additionally, traditional ANN models are susceptible to overfitting, resulting in subpar accuracy. Deep Belief Network (DBN) is a highly precise algorithm in deep learning. However, the intricate combination of hyperparameters often leads to limited generalization ability and model robustness when correcting DEM. The present study proposes an error prediction model based on the DBN optimized by Particle Swarm Optimization (PSO) for SRTM DEM correction. By utilizing the PSO algorithm, we aim to identify the optimal combination of hyperparameters of DBN, including the number of neurons in the hidden layer and the learning rates. The experiment focuses on two regions in Hunan Province, China, characterized by abundant vegetation cover. The reference data utilized for comparison is ICESat/GLAS data. The experimental results demonstrate that the mean error (ME) and root mean square error (RMSE) of the SRTM DEM corrected by the proposed algorithm in these two regions are significantly reduced by 93.5%–96.0% and 21.5%–23.5%, respectively. Moreover, there is an improvement of over 26.1% in accuracy within complex terrain areas. Specifically, in broadleaf forest, the PSO-DBN method exhibits a remarkable accuracy improvement of 26.2%, while the DBN-corrected SRTM DEM shows an improvement of 15.3%. In coniferous forest, the PSO-DBN method achieves an accuracy improvement of 14.8%, whereas the DBN-corrected SRTM DEM demonstrates a gain of 5.8%. The approach provides a more effective and robust tool for correcting SRTM DEM or other similar DEMs over vegetated mountain areas.

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Section: Introductionmentioning

confidence: 99%

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

Sun,

Zhou,

Xie

et al. 2023

Forests

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“…Given the complex terrain of Chongqing, using suitable correction methods to improve DEM accuracy is crucial. These results highlight the significance of choosing suitable DEM products and correction methods for terrain modeling and related applications [62,63].…”

Section: The Impact Of Spatial Resolution On Dem Accuracymentioning

confidence: 87%

Vertical Accuracy Assessment and Improvement of Five High-Resolution Open-Source Digital Elevation Models Using ICESat-2 Data and Random Forest: Case Study on Chongqing, China

Xu,

Li,

Peng

et al. 2024

Remote Sensing

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Digital elevation models (DEMs) are widely used in digital terrain analysis, global change research, digital Earth applications, and studies concerning natural disasters. In this investigation, a thorough examination and comparison of five open-source DEMs (ALOS PALSAR, SRTM1 DEM, SRTM3 DEM, NASADEM, and ASTER GDEM V3) was carried out, with a focus on the Chongqing region as a specific case study. By utilizing ICESat-2 ATL08 data for validation and employing a random forest model to refine terrain variables such as slope, aspect, land cover, and landform type, a study was undertaken to assess the precision of DEM data. Research indicates that spatial resolution significantly impacts the accuracy of DEMs. ALOS PALSAR demonstrated satisfactory performance, reducing the corrected root mean square error (RMSE) from 13.29 m to 9.15 m. The implementation of the random forest model resulted in a significant improvement in the accuracy of the 30 m resolution NASADEM product. This improvement was supported by a decrease in the RMSE from 38.24 m to 9.77 m, demonstrating a significant 74.45% enhancement in accuracy. Consequently, the ALOS PALSAR and NASADEM datasets are considered the preferred data sources for mountainous urban areas. Furthermore, the study established a clear relationship between the precision of DEMs and slope, demonstrating a consistent decline in precision as slope steepness increases. The influence of aspect on accuracy was considered to be relatively minor, while vegetated areas and medium-to-high-relief mountainous terrains were identified as the main challenges in attaining accuracy in the DEMs. This study offers valuable insights into selecting DEM datasets for complex terrains in mountainous urban areas, highlighting the critical importance of choosing the appropriate DEM data for scientific research.

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“…In this experiment, the dataset of stacking and bagging was divided into 5 folds and two layers of training were used. In fact, the number of layers of training and the number of meta-learners can be determined according to the needs [34].…”

Section: Machine Learning Modelmentioning

confidence: 99%

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

Xie,

Wang,

Chen

et al. 2023

Remote Sensing

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The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

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SRTM DEM Correction Using Ensemble Machine Learning Algorithm

Cited by 10 publications

References 61 publications

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

SRTM DEM Correction Based on PSO-DBN Model in Vegetated Mountain Areas

Vertical Accuracy Assessment and Improvement of Five High-Resolution Open-Source Digital Elevation Models Using ICESat-2 Data and Random Forest: Case Study on Chongqing, China

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

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