Mapping and Classification of the Liaohe Estuary Wetland Based on the Combination of Object-Oriented and Temporal Features

作者：Sien Guo, Ziyi Feng, Peng Wang, Jie Chang, Hao Han, Haifu Li, Chunling Chen, Wen Du · 发表于：IEEE Access · 年份：2024 · DOI：10.1109/access.2024.3389935 · 被引用次数：6 · 研究领域：Land Use and Ecosystem Services、Remote Sensing in Agriculture、Remote Sensing and Land Use

For the protection, restoration, and sustainable management of wetland ecosystems, precision in extracting high-quality wetland land cover information is crucial. This study focused on the National Nature Reserve of Liaohe Estuary in Panjin City, Liaoning Province, China. To overcome the challenge of spectral similarity among wetland land covers and the occurrence of the "salt-and-pepper" effect where certain land parcels get misclassified into multiple categories by conventional methods, an approach combining object-oriented techniques and temporal features was employed for accurate wetland land cover classification. The analysis utilized multi-temporal Sentinel-2 multispectral images. Initially, the images underwent segmentation using the SNIC method to generate uniform polygons, effectively mitigating misclassification issues. Subsequently, texture, geometry, band reflectance, and spectral deviation features were extracted for each segmented object. A total of 57 features, including vegetation and moisture components, were integrated to construct temporal characteristics. By applying the Random Forest (RF) algorithm in combination with Recursive Feature Elimination (ERT), 18 significant features influencing wetland extraction were identified. These selected features were then utilized to train a Random Forest (RF) model for classifying wetland land cover in the study area. The findings revealed that the integrated object-oriented and temporal feature classification approac...