Parallel implementation of a hyperspectral feature extraction method based on Gabor filter

Land-cover classification is one of many applications involved with remote sensing. This task usually requires image processing to compute relevant features, which will be the input for a classifier. Some feature extraction algorithms can become complex in processing time since remote sensing images such as hyperspectral ones consist of a large number of bands. This represents a delay in the classification stage. Consequently, the final results of the land-cover classification could not be obtained in real-time. Therefore, a parallel implementation of the feature extraction stage may contribute to the real-time classification process of hyperspectral images by reducing the computing time of the features. In the specific case of hyperspectral images, the features can be categorized in spatial and spectral, being the algorithms to obtain the spatial ones more susceptible to increase their computational time due to parameters such as the neighborhood size. One spatial-feature extraction method that has led to desirable classification results in image processing is the Gabor filter. Nonetheless, it implicates a high computational cost because of the application of the filter bank composed of various rotations and scales. This work aims to propose a parallel implementation of a Gabor filter feature extraction method for hyperspectral images over a Graphics Processing Unit (GPU) and multi-core Central Process Unit (CPU). The performance of the implementation is compared with the non-parallel version of the process in terms of computing time and time complexity of the algorithms. Furthermore, the feature extraction method is evaluated with a Support Vector Machine (SVM) using overall accuracy and kappa coefficient as quality metrics.