Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Zhu, Hao; Xu, Yuexue; Cheng, Yu; Liu, Haiying; Zhao, Yipeng

doi:10.1007/s11707-019-0751-2

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…Based on the comprehensive geohazard index and the geomorphic characteristics, the geohazard regionalization scheme was determined, as shown in Figure 13 and Table 7 (Zhu et al 2019).…”

Section: Geohazards Regionalizationmentioning

confidence: 99%

Geohazards regionalization along highways in Shandong Province, China

Yang

Zhao

Cheng

2020

Geomatics, Natural Hazards and Risk

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The disturbance of the highway construction to the terrain and the natural environmental factors are the main causes of the geohazards along the highway. We collected geohazards along the highway in Shandong Province and put forward the comprehensive geohazard index to reflect the risk of geohazards along the highway. The index consists of an intensity index describing the development of geohazards and a sensitivity index describing the potential risk of geohazards. The article uses an incomplete additive language matrix to calculate the weight of each index and factor. Based on the Getis-Ord Gi Ã statistic tool, the spatial distribution of landslides and collapses, debris flows, ground collapses, and unstable slopes are analyzed, and the geohazard intensity index is calculated by spatial superposition. The relationship between slope gradient, rainfall, vegetation coverage, rock type and geohazards is analyzed, and the geohazard sensitivity index is calculated by spatial superposition. Based on the comprehensive geohazard index and geomorphological characteristics, Shandong Province is divided into 4 geohazard first-level regions and 11 geohazard second-level regions, providing a basis for highway planning and construction.

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“…Based on the comprehensive geohazard index and the geomorphic characteristics, the geohazard regionalization scheme was determined, as shown in Figure 13 and Table 7 (Zhu et al 2019).…”

Section: Geohazards Regionalizationmentioning

confidence: 99%

Geohazards regionalization along highways in Shandong Province, China

Yang

Zhao

Cheng

2020

Geomatics, Natural Hazards and Risk

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“…At present, the studies on terrain texture are mainly based on the calculation method of image texture. The terrain texture is used for feature calculation and index extraction and is used for regional terrain analysis and landform classification [5][6][7] . However, the fundamental reason for the different types of landforms is the relief terrain.…”

Section: Introductionmentioning

confidence: 99%

Visualization model of relief texture based on DEM

Zhang²,

Tang³

et al. 2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

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Texture has a wide range of applications in the field of image processing. The application of existing texture research methods in DEM is mostly texture feature extraction and index calculation. However, the formation of landforms is caused by terrain relief. The existing texture analysis methods generally have a deficiency in the reflection on the human visual system. In this paper, the visual boundary lines, visual outline lines, and skeleton lines were used as the DEM relief textures that can visually reflect terrain relief. Then the extraction method is refined in conjunction with digital terrain analysis theory. Finally, a multi-dimensional visualization model of DEM relief texture was built, which achieves a better visual effect and provides new ideas for digital terrain analysis, and also has a wide range of popularization and application value.

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“…Several ML models have been used in geomorphology, such as random forests (Shruthi et al, 2014; Stumpf & Kerle, 2011), support vector machines (Peng et al, 2014; Zhu et al, 2019), artificial neural networks (Iglesias et al, 2009; Palafox et al, 2017), among others. The use of various algorithms to choose the model with the best performance is a common approach due to the intrinsic characteristics of each landscape (Marmion et al, 2008; Rahmati et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluation of machine learning algorithms to classify and map landforms in Antarctica

Siqueira

Veloso

Filho

et al. 2021

Earth Surf Processes Landf

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The detailed geomorphology of ice‐free landscapes of Antarctica is key to understanding how their highly fragile environments respond to climate change, at different temporal and spatial scales. Despite the recent advances in geomorphological studies of ice‐free areas, machine learning applications to produce landform maps are still scarce on the Antarctic continent. In this study, we evaluated the predictive performance of different supervised machine learning algorithms to produce digital geomorphological maps in Vega Island—Antarctic Peninsula region. We tested six different models: average artificial neural networks, C5.0 decision tree, random forest, support vector machine, supervised self‐organizing map and weighted k‐nearest neighbours. We used an initial set of 54 geomorphometric and spectral predictors, from which redundant variables with Pearson correlation coefficient >|0.95| were removed, and only the most important predictors for each model were selected using recursive feature elimination. For training, we ran each model 100 times and predictions were assessed by the kappa and global accuracy values. The best predictors were the Red Edge 6 and SWIR 11 bands, roughness concentration index, elevation and drainage density. The decision trees C5.0 and random forest had the best performance, with average validation kappa of 0.85 ± 0.03 and 0.84 ± 0.03, respectively, evidencing excellent prediction. Despite the similar performance, random forest showed greater uncertainty degree and accuracy when classifying complex landforms, attesting to its great robustness. From sensitivity and specificity values, we observed that the glaciers and talus showed higher accuracy, whereas cryoplanated platforms and scree slopes had the worst classification. The presented methodology optimized the classification by selecting the most important predictors, assessing accuracy and evaluating uncertainty. The results indicated that machine learning methods have great potential to produce geomorphological mappings in the Antarctic ice‐free areas, as a promising tool to provide detailed information on remote, harsh polar environments.

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Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Cited by 12 publications

References 34 publications

Geohazards regionalization along highways in Shandong Province, China

Geohazards regionalization along highways in Shandong Province, China

Visualization model of relief texture based on DEM

Evaluation of machine learning algorithms to classify and map landforms in Antarctica

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