Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea

Abstract

Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampling technique (smote) with Random Forest to classify land cover classes in a small agricultural catchment in South Korea using modis time series. This area faces a major soil erosion problem and policy measures encourage farmers to replace annual by perennial crops to mitigate this issue. Our major goal was therefore to improve the classification performance on annual and perennial crops. We compared four different classification scenarios on original imbalanced and synthetically oversampled balanced data to quantify the effect of smote on classification performance. smote substantially increased the true positive rate of all oversampled minority classes. However, the performance on minor classes remained lower than on the majority class. We attribute this result to a class overlap already present in the original data set that is not resolved by smote. Our results show that resampling algorithms could help to derive more accurate land use and land cover maps from freely available data. These maps can be used to provide information on the distribution of land use classes in heterogeneous agricultural areas and could potentially benefit decision making.

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

“…However, several previous studies reported results consistent with our findings. Reference [25] reported an increase in F-score and G-mean when oversampling was applied, while Accuracy did not improve. Similarly, results obtained in [5] demonstrated increased classification performance when using SMOTE.…”

“…Similarly, the application of SMOTE improved the classification results. Other examples of the successful application of SMOTE in remote sensing can be found in [25,26].…”

Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

“…Once collected, the samples were statistically filtered (with the 80th percentile function) and visually inspected to remove inadequate training samples. Stratified sampling and statistical filtering are necessary to address imbalanced class problems [50], allowing the removal of outliers from the sample bag. The presence of imbalanced Having set the refined training region,~1000 stratified random samples were distributed per class, mangrove and non-mangrove, per sector per year.…”

“…Stratified sampling and statistical filtering are necessary to address imbalanced class problems [50], allowing the removal of outliers from the sample bag. The presence of imbalanced classes within the coastal region is a probable scenario, as water surface samples may, in general, greatly surpass other rare class occurrences (e.g., mangrove cover).…”

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

“…It should be also noted that no training/test samples from the cloud-contaminated parts of the 50 cm image were collected, because the underlying LULC types were not apparent in those areas. Since the training data gathered for both the images were imbalanced, we applied the Synthetic Minority Oversampling Technique (SMOTE) [54] to artificially balance the training set, as this was found beneficial in past studies using imbalanced data [55,56] and machine learning classifiers [57].…”