An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning

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.

Hydrodynamic Modelling of Floods and Estimating Socio-economic Impacts of Floods in Ugandan River Malaba Sub-catchment

“…The obtained maps were already classified by NFA by unsupervised classification. This approach has recently been recommended by Ma et al (2020) due to the lower field data sample requirements as compared to the supervised method. In the present study, LULC changes over the study area from 2010 to 2017 were quantified prior to hydrodynamic modelling (Fig.…”

Evaluation of Different Satellite Images for Urban Land Use Analysis: A Case Study of Greater Manchester, United Kingdom

“…Unsupervised and supervised classification approach has been at the core of land cover mapping [31,32]. Unsupervised classification identifies structures or natural groups within a multispectral data, while supervised is the process by which samples of known identity are used to classify pixels of known identity.…”

Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping

“…Given SITS data, a major goal of research is to achieve more precise land cover classification results by further exploring the temporal information contained in SITS [20]. A quick review of the literature shows that many methods regarding the classification of SITS have been proposed in recent years [1,2,[21][22][23][24][25][26][27][28][29][30][31][32][33][34], and these methods can be divided into three major categories: neural-network-based methods, similarity-measure-based methods, hybrid and other methods.…”

“…Currently, Dynamic Time Warping (DTW) [26,27] and its variants [28], for example, time-weighted DTW [29,30], have become some of the most widely used similarity measures for SITS. Hybrid methods include combining different classification paradigms [31], fusing time series data from different satellites with different spatiotemporal resolutions [32] and constructing new features with machine-learning models [33,34] or phenological models [31]. Despite the variety of classification methodologies, one common point for most of them is that they propose a high requirement for the amount and quality of labeled training samples.…”