Xiaoxue Ma

TL;DR: It is found that supervised object- based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework, and spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest shows the best performance inobject-based classification.

TL;DR: A strategy for the semi-automatic optimization of object-based classification is developed, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size and suggests that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation.

TL;DR: Evaluating the effect of the advanced feature selection methods of popular supervised classifiers for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA).

TL;DR: It is concluded that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods.

TL;DR: In this paper, inexact stochastic multiple objective programming (ISMOP) was applied to analyze the optimization of industrial structure based on water environmental carrying capacity in Huai River Basin within Shandong Province (HRBSP).