Spatial-SMOTE: An Approach for Handling Class Imbalance in Spatial Time Series Data

Data imbalance handling is important to have unbiased learning during model training of classification tasks. Synthetic minority over-sampling technique (SMOTE) is primarily used for data imbalance handling. Conventional SMOTE algorithm and its variants mainly deal with upsampling the class data that is based solely on the amplitude values of features and their neighbors. However, the spatio-temporal data corresponding to satellite remote sensing images, comes with the additional location information, i.e. longitude and latitude. This has to be incorporated in the data upsampling case in order to have semantically and physically useful data. Hence, we propose a new pipeline named, ‘Spatial-SMOTE’ to upsample the data by retaining the significance of spatial distribution aspect in the overall upsampling process. The effectiveness of this approach is shown on land-use land-cover classification task using the time series data for a particular study area. We identified the relation between different classes based on their semantic distances and formulated two cases- one with high semantic distance and other with low. It is to be observed that the proposed method of Spatial-SMOTEing the minority class works well for both the cases. We also tested the effectiveness of the proposed approach over synthetically induced class imbalances for both low and high semantically differing classes.