Object-Oriented Crop Classification Using Time Series Sentinel Images from Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1353
• Main Authors: Xue, Hanyu, Xu, Xingang, Zhu, Qingzhen, Yang, Guijun, Long, Huiling, Li, Heli, Yang, Xiaodong, Zhang, Jianmin, Yang, Yongan, Xu, Sizhe, Yang, Min, Li, Yafeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; Algorithms; China; Classification; Cloud computing; Clustering; Crop identification; Crops; Fertility; Google Earth Engine; Image classification; Image segmentation; Industrial parks; Internet; Land use; Land use classification; object-oriented; Pixels; Precipitation; Radar; random forest classification; Remote sensing; Rice; Satellite imagery; Satellites; Sentinel images; SNIC algorithm; support vector machine classification; Support vector machines; Time series; time series analysis; Wheat; Mongolia; China

The resulting maps of land use classification obtained by pixel-based methods often have salt-and-pepper noise, which usually shows a certain degree of cluttered distribution of classification image elements within the region. This paper carries out a study on crop classification and identification...