Obtaining relevant classification results for hyperspectral images depends on the quality of the data and the proposed selection of the samples and descriptors for the training and testing phases. We propose a hyperspectral image classification machine learning framework based on image processing techniques for denoising and enhancement and a parallel approach for the feature extraction step. This parallel approach is designed to extract the features by employing the wavelet transform in the spectral domain, and by using Local Binary Patterns to capture the texture-like information linked to the geometry of the scene in the spatial domain. The spectral and spatial features are concatenated for a Support Vector Machine-based supervised classifier. For the experimental validation, we propose a controlled sampling approach that ensures the independence of the selected samples for the training data set, respectively the testing data set, offering unbiased performance results. We argue that a random selection applied on the hyperspectral dataset to separate the samples for the learning and testing phases can cause overlapping between the two datasets, leading to biased classification results. The proposed approach, with the controlled sampling strategy, tested on three public datasets, Indian Pines, Salinas and Pavia University, provides good performance results.