This thesis is focused on deep learning-based, pixel-wise classification of hyperspectral images (HSI) in remote sensing. Although presence of many spectral bands in an HSI provides a valuable source of features, dimensionality reduction is often performed in the pre-processing step to reduce the correlation between bands. Most of the deep learning-based classification algorithms use unsupervised dimensionality reduction methods such as principal component analysis (PCA). However, in this thesis in order to take advantage of class discriminatory information in the dimensionality reduction step as well as power of deep neural network we propose a new method that combines a supervised dimensionality reduction technique, principal component discriminant analysis (PCDA) and deep learning. Furthermore, in this thesis in order to overcome the common problem of inadequacy of labeled samples in remote sensing HSI classification, we propose a spectral perturbation method to augment the number of training samples and improve the classification results. Since combining spatial and spectral information can dramatically improve HSI classification results, in this thesis we propose a new spectral-spatial feature vector. In our feature vector, based on their proximity to the dominant edges, neighbors of a target pixel have different contributions in forming the spatial information. To obtain such a proximity measure, we propose a method to compute the distance transform image of the input HSI. We then improved the spatial feature vector by adding extended multi attribute profile (EMAP) features to it. Classification accuracies demonstrate the effectiveness of our proposed method in generating a powerful, expressive spectral-spatial feature vector.