Shi et al. (2026) Automatic Water-Land Segmentation Algorithm via Weighted Mixture Model With Dual Spatial Constraints

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Xue Shi, Yufei Wang
• DOI: 10.1109/jstars.2026.3670465

Research Groups

[Not provided in the given text.]

Short Summary

This paper introduces an automatic algorithm designed for water-land segmentation, which employs a weighted mixture model augmented with dual spatial constraints to enhance classification accuracy.

Objective

• To develop and evaluate an automatic and robust algorithm for accurate water-land segmentation.

Study Configuration

• Spatial Scale: [Not provided in the given text, but typically pixel-level or image-level for segmentation tasks.]
• Temporal Scale: [Not provided in the given text, likely applicable to static imagery or time-series analysis without specific temporal resolution mentioned.]

Methodology and Data

• Models used: Weighted Mixture Model, Dual Spatial Constraints (as integral components of the segmentation algorithm).
• Data sources: [Not provided in the given text, but typically remote sensing imagery or aerial photographs.]

Main Results

[Not provided in the given text.]

Contributions

• Development of a novel automatic water-land segmentation algorithm that integrates a weighted mixture model with dual spatial constraints.
• Potential for improved performance in water-land classification through the proposed methodological enhancements.

Funding

[Not provided in the given text.]

Citation

@article{Shi2026Automatic,
  author = {Shi, Xue and Wang, Yufei and Li, Mengmeng},
  title = {Automatic Water-Land Segmentation Algorithm via Weighted Mixture Model With Dual Spatial Constraints},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3670465},
  url = {https://doi.org/10.1109/jstars.2026.3670465}
}