Parallel artificial neural networks using wavelet-based features for classification of remote-sensing hyperspectral images

Remote sensing consists on the acquisition of a specific area information. This process serves as a base to detect and monitor objects without being in physical contact with a landscape. One of the many signal representations that can be captured through this process is the hyperspectral image. This kind of image is characterized by its large number of bands, which means that a single pixel may have hundreds of values. In order to identify the objects registered in the images, their pixels need to be classified. The classification of hyperspectral images represents a high computational cost due to their dimensions. This study aims to propose a time optimization of the classifcation process of these images. For this reason, a comparison between feature extraction methods using wavelet filters, such as Haar, Daubechies, Biorthogonal, Coiflets and Symlets, is performed in order to apply a shrinkage of the image's dimension. Furthermore, three Artificial Neural Network architectures are proposed with the objective of classify the images using the features based in the Wavelet Transform. These architectures are implemented in a parallel programming model to be executed over a Graphics Processing Unit. Additionally, a multi-thread scheme programmed to be used in a multi-core Central Process Unit variation is presented. Both implementations and a non-parallel version of the methods are compared using algorithmic computational complexity, computing time performance, overall accuracy and kappa coefficient. To measure the performance of the methods, experiments using cross-validation and different number of samples to train the classifiers and are carried out.