Diversity-accuracy assessment of multiple classifier systems for the land cover classification of the Khumbu region in the Himalayas

Hanson, Charisse Camacho; Brabyn, Lars; Gurung, Sher Bahadur

doi:10.1007/s11629-021-7130-7

Cited by 4 publications

(2 citation statements)

References 76 publications

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“…In their work, they used the RF algorithm to classify the grasslands and Google Earth Engine to perform the NDVI time series curve analysis. Hanson et al 37 utilised different base ML algorithms to design the ensemble model for the LULC classification on Sentinel-2 data, utilising different spectral bands, spectral indices, and topographic information for each pixel present in the image for the feature extraction. Tang et al 38 proposed the two-level classification approach for the land cover changes in cultural heritages on VHR images.…”

Section: Related Workmentioning

confidence: 99%

Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

Aryal,

Sitaula,

Frery

2023

Sci Rep

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Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value $$\le$$ ≤ 0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.

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Section: Related Workmentioning

confidence: 99%

Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

Aryal,

Sitaula,

Frery

2023

Sci Rep

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“…The RF and XGboost classifiers have shown outstanding performance in classification of the satellite data [75][76][77][78][79][80]. Researchers have reported higher performance of the fusion of multiple classification results in land cover and vegetation mapping [81,82].…”

Section: Machine Learning and Mappingmentioning

confidence: 99%

An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution

Sharma

2022

Remote Sensing

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This paper presents two categories of features extraction and mapping suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, for the differentiation and mapping of land cover and community-level vegetation types. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features. The performance of the ultra-resolution features extraction suite was compared with the very high-resolution features extraction suite that involves the calculation of radiometric indices and color-transformation of the multi-spectral image. This research was implemented in three mountainous ecosystems located in a cool temperate region. Three machine learning classifiers, Random Forests, XGBoost, and SoftVoting, were employed with a 10-fold cross-validation method for quantitatively evaluating the performance of the two suites. The ultra-resolution suite provided 5.3% more accuracy than the very high-resolution suite using single-date autumn images. Addition of summer images gained 12.8% accuracy for the ultra-resolution suite and 13.2% accuracy for the very high-resolution suite across all sites, while the ultra-resolution suite showed 4.9% more accuracy than the very high-resolution suite. The features extraction and mapping suites presented in this research are expected to meet the growing need for differentiating land cover and community-level vegetation types at a large scale.

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GIS and geophysics: an integrative and inter-evaluating approach towards groundwater assessment

Kumar

Warwade

2022

Geocarto International

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Diversity-accuracy assessment of multiple classifier systems for the land cover classification of the Khumbu region in the Himalayas

Cited by 4 publications

References 76 publications

Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia

An Ultra-Resolution Features Extraction Suite for Community-Level Vegetation Differentiation and Mapping at a Sub-Meter Resolution

GIS and geophysics: an integrative and inter-evaluating approach towards groundwater assessment

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