An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning

Zhe Ma,Tianwei Ren,Shaoming Li,Lin Zhang,Yuanyuan Zhao,Xiaodong Zhang,Diyou Liu,Zhe Liu

doi:10.3390/ijgi9110648

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. 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.

Highlights

Food security is crucial for the livelihood and economic development of the global population
In the current paper, we propose a crop classification method based on principal components isometric binning (PCIB), taking the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China as the study areas
We explore the feasibility of our proposed method via comparisons with the traditional random forest, K-means and ISODATA approaches, and demonstrate PCIB as a promising approach for crop classification mapping without the requirement of seasonal sample data

Summary

Introduction

Food security is crucial for the livelihood and economic development of the global population. The majority of current remote sensing-based crop classification methods fall into the category of supervised classification, including machine learning [5,6,7,8,9,10,11,12] and deep learning [13,14] Such methods require the collection of ground sample data in order to train the classification model. The majority of the current crop classification techniques require current-year samples, while the sample collection, data processing and additional steps are highly time consuming, leading to a lag in classification data This prevents the effective application of the classification results for the current agricultural season

Methods

Results

Discussion

Conclusion