7 Papers
19 Citations
Jun Wang is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 3, co-authored 5 publications.
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Papers
An Improved Hybrid Segmentation Method for Remote Sensing Images
TL;DR: This paper develops an improved remote sensing image segmentation method that is a hybrid method (split-and-merge), and the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation.
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Local Scale-Guided Hierarchical Region Merging and Further Over- and Under-Segmentation Processing for Hybrid Remote Sensing Image Segmentation
TL;DR: Li et al. as mentioned in this paper developed a hybrid image segmentation method with local scale-guided hierarchical region merging and further over- and under-segmentation processing, where the primitive segmentation was produced and then stratified into layers with different land covers.
Remote Sensing-Guided Sampling Design with Both Good Spatial Coverage and Feature Space Coverage for Accurate Farm Field-Level Soil Mapping
TL;DR: This paper proposes a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes and compares it to regular grid sampling and k-means sampling.
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Double-Variance Measures: A Potential Approach to Parameter Optimization of Remote Sensing Image Segmentation
TL;DR: In this paper, double-variance (DV) measures were proposed for recognizing more suitable SPs and two combination strategies, F-measure and local peak (LP), were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively.
Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images
TL;DR: In this article, the authors proposed an approach to recognize semantic geo-objects by iteratively merging single geo objects, and the optimal scale of the semantic objects is determined by identifying the optimal scales of single geoobjects and using them as the initiation point of the reset scale parameter optimization interval.
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