An Object-Based Approach for Mapping Crop Coverage Using Multiscale Weighted and Machine Learning Methods

Abstract

Accurate mapping of crop distribution on Earth's surface aids in predicting grain production. Pattern classification along with remote sensing imagery can facilitate traditional manual field measurement techniques using machine learning. With the rapid increase in satellite sensor resolution, the object-based classification paradigm has increasingly been applied. However, scale parameter selection is always a difficult part of the object-based classification. Based on ensemble learning, this study proposes a classification method using the multiscale object-based weighted method which includes manual digitizing of crop distribution in the southern region of Jishan County, Shanxi Province, China, applying Gaofen-2 (GF-2) images. This method initially uses estimations of the scale parameter (ESP) tool to select “good” scales, defined here as “preferred” scales, after which feature subsets are screened by each preferred scale as the input of multiple classifiers and classifies. Finally, all classification results are then fused. Our research results indicate that: 1) Feature importance values are sorted differently at different preferred scales; 2) accuracy differences become clear when different preferred scales are combined with different classifiers, and determining the “best” single appropriate scale is generally difficult; 3) accuracy of the multiscale weighted classification method is higher compared to the single preferred scale approach. Furthermore, ensemble learning can be achieved using this method on multiple scales and on multiple classifiers. With this method, procedures that necessitate the selection of segmentation scales and the selection and optimization of classifiers can be skipped altogether.