Parallelized Inter-Image k-Means Clustering Algorithm for Unsupervised Classification of Series of Satellite Images

Abstract

As the volume of satellite images increases rapidly, unsupervised classification can be utilized to swiftly investigate land cover distributions without prior knowledge and to generate training data for supervised (or deep learning-based) classification. In this study, an inter-image k-means clustering algorithm (IIkMC), as an improvement of the native k-means clustering algorithm (kMC), was introduced to obtain a single set of class signatures so that the classification results could be compatible among multiple images. Because IIkMC was a computationally intensive algorithm, parallelized approaches were deployed, using multi-cores of a central processing unit (CPU) and a graphics processing unit (GPU), to speed up the process. kMC and IIkMC were applied to a series of images acquired in a PlanetScope mission. In addition to the capability of the inter-image compatibility of the classification results, IIkMC could settle the problem of incomplete segmentation and class canceling revealed in kMC. Based on CPU parallelism, the speed of IIkMC improved, becoming up to 12.83 times better than sequential processing. When using a GPU, the speed improved up to 25.53 times, rising to 39.00 times with parallel reduction. From the results, it was confirmed IIkMC provided more reliable results than kMC, and its parallelism could facilitate the overall inspection of multiple images.