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Texture Classification of Very High Resolution UAS Imagery Using a Graphics Processing Unit

The high computational demands of classifying very high-resolution (VHR) imagery calls for efficient implementations such as parallel processing, in particular, for computing on Graphics Processing Units (GPUs). In this work, a problem of classifying invasive Phragmites australis from other vegetation is considered. This study uses VHR imagery with a ground sampling distance of 25 centimeters acquired using a small-unmanned aerial system. We studied the use of Compute Unified Device Architecture for two feature extraction methods with a probabilistic neural network classifier. The texture extraction and parameter optimization on GPUs uses a divide and conquer strategy. The results of the study show the use of GPUs significantly increased speed when compared to a traditional Central Processing Unit based classification setup.