A methodology for balancing the preservation of area, shape, and topological properties in polygon-to-raster conversion

Huo, Xiaolong; Zhou, Chen; Xu, Yunyun; Li, Manchun

doi:10.1080/15230406.2021.1991478

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…For classified land-use data, effective approaches to reduce the loss in spatial properties have been proposed. Specifically, many studies have focused on the vector-to-raster conversion and have put forward different methods to preserve an individual spatial property of area, topology, or shape [75,76], and to achieve balanced preservation of various properties [77]. In addition, some researchers have paid attention to the resampling process and presented an area-preserving method for maintaining the area property after resampling categorical raster data [78].…”

Section: A Limitationsmentioning

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

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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.

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