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Open AccessArticle

An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning

by 1, 1,2,3, 1,2,3, 1, 1, 1, 1,2,3 and 1,2,3,*
1
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
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
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 648; https://doi.org/10.3390/ijgi9110648
Received: 24 September 2020 / Revised: 20 October 2020 / Accepted: 26 October 2020 / Published: 29 October 2020
(This article belongs to the Special Issue Geospatial Artificial Intelligence)
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. Bins of the same category are subsequently merged as a class label. Multitemporal Gaofen 1 satellite (GF-1) remote sensing images were collected over the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China in order to map crop types in 2016 and 2017. Our proposed method was compared with commonly used classifiers (random forest, K-means and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA)). Results demonstrate PCIB and random forest to have the highest classification accuracies, reaching 82% in 2016 in the southwest of Hulin City. In Luobei County in 2016, the accuracies of PCIB and random forest were determined as 81% and 82%, respectively. It can be concluded that the overall accuracy of our proposed method meets the basic requirements of classification accuracy. Despite exhibiting a lower accuracy than that of random forest, PCIB does not require a large field sample size, thus making it more suitable for large-scale crop mapping. View Full-Text
Keywords: PCIB; random forest; K-means; ISODATA; crop mapping; unsupervised classification; GF-1 PCIB; random forest; K-means; ISODATA; crop mapping; unsupervised classification; GF-1
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Ma, Z.; Liu, Z.; Zhao, Y.; Zhang, L.; Liu, D.; Ren, T.; Zhang, X.; Li, S. An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. ISPRS Int. J. Geo-Inf. 2020, 9, 648.

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