International Journal of Geo-Information Article An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning Zhe Ma 1, Zhe Liu 1,2,3 , Yuanyuan Zhao 1,2,3, Lin Zhang 1, Diyou Liu 1 , Tianwei Ren 1, Xiaodong Zhang 1,2,3 and Shaoming Li 1,2,3,* 1 College of Land Science and Technology, China Agricultural University, Beijing 100083, China;
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[email protected] (X.Z.) 2 Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China 3 Key Laboratory for Agricultural Land Quality, Ministry of Natural Resources of the People’s Republic of China, Beijing 100083, China * Correspondence:
[email protected]; Tel.: +86-010-627-37554 Received: 24 September 2020; Accepted: 26 October 2020; Published: 29 October 2020 Abstract: The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is consequently costly in terms of time and money. In order to reduce the need for sample size, this paper proposes an unsupervised classification method based on principal components isometric binning (PCIB). In particular, principal component analysis (PCA) dimensionality reduction is applied to the classification features, followed by the division of the top k principal components into equidistant bins.