Combined particle swarm optimization and linear discriminant analysis for landslide image classification: application to a case study in Taiwan

Wan, Shiuan; Chang, Shih-Hsun

doi:10.1007/s12665-014-3050-y

Cited by 4 publications

(3 citation statements)

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“…Linear discriminant analysis (LDA) is a classical statistical approach for classifying samples of unknown classes [11]. LDA is related to machine learning to find the linear combination of features which best separate two or more series of classes of objects.…”

Section: Ldamentioning

confidence: 99%

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The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

Wan

Wang

2020

Agriculture

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The analysis, measurement, and computation of remote sensing images often require enhanced unsupervised/supervised classification approaches. The goal of this study is to have a better understanding of (a) the classification performance of multispectral image and hyperspectral image data; (b) the classification performance of unsupervised and supervised models; and (c) the classification performance of feature selection among different models. More specifically, the multispectral images and hyperspectral images with high spatial resolution are well accepted for improving land use and classification. Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN), linear discriminant analysis (LDA), and back-propagation neural network (BPN). Then the feature selection (principle component analysis, PCA) on four classifiers is studied. The results show that the image material of CASI-1500 classification accuracy is slightly better than that of WorldView-2. The overall classification of BPN is the best, the overall data has a κ value of 0.89 and the overall accuracy is 97%. The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time.

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Section: Ldamentioning

confidence: 99%

“…The ability to interpret different types of characteristic spectra is solved by increasing ancillary information [5][6][7]11,12] to improve the classification accuracy of an image. Researchers usually use supervised classifiers to resolve image processing problems [10,[13][14][15].…”

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confidence: 99%

The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

Wan

Wang

2020

Agriculture

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“…Compared to the traditional geological survey methods, such as landslide field reconnaissance, landslide spatial prediction is more convenient and efficient, due to the integration of geographical information systems (GIS) technology and statistical analysis principles. The spatial prediction of landslide susceptibility mapping is considered as one of the most important steps for landslide hazard mitigation and management [ 4 ], which has encouraged research towards knowledge-driven and data-driven models [ 5 ]. Knowledge-driven models, such as analytic hierarchy process (AHP) and fuzzy mathematics [ 5 , 6 ], are based on the analysis of landslide formation mechanism(s), and expert experience and knowledge are used to choose the most important environmental factors of landslides and quantitative weight values.…”

Section: Introductionmentioning

confidence: 99%

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Wang

Niu

et al. 2016

IJERPH

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In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

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Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study

Atasever,

Tercan

2023

Environ Sci Pollut Res

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Combined particle swarm optimization and linear discriminant analysis for landslide image classification: application to a case study in Taiwan

Cited by 4 publications

References 29 publications

The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study

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