Rice phenology monitoring via ensemble classification for an extremely imbalanced multiclass dataset of hybrid remote sensing

Abstract

This research provides a new approach for monitoring crops, especially rice planting, using hybrid remote sensing by aligning direct observations via Area Sampling Frame (ASF) surveys and earth observations via the LANDSAT 8 spectral indices. ASF surveys classify the land cover into eight groups, four associated with rice phenology. There's probably an imbalance between the classes in the multi-class ASF dataset. Often, when dealing with such extremely multiclass datasets, the performance of conventional classifiers is not adequate. Therefore, this study adopts a resampling technique to improve ensemble classification performance for handling extremely imbalanced multiclass ASF datasets. This research has added resampling methods, ROS (Random Over Sampling) and SCUT (SMOTE and Cluster-based Undersampling Technique), to the ensemble model, namely Bagging and Random Forest (RF), which have an impact on improving classification performance. The results showed that ROS-RF performed well in classifying multiclass on the ASF Survey with accuracy (90%), average sensitivity (90%), average specificity (99%), and balanced accuracy (94%). The variable importance (VI) with the highest ranking based on the Mean Decrease Gini (MDG) is the Modified Normalized Difference Water Index, which was recorded by the LANDSAT 8 satellite in one period before (MDWIt-1). However, the nine variables included in the classification generally contribute almost the same level of importance. The recommended classification strategy for highly imbalanced multi-class datasets is ROS-RF, which contributes to improving RF classification performance. The initial step was repeating minority samples until the class distribution was balanced before executing the classification stage.