With the recent advances made in the Earth Observation (EO) field, the use of remote sensing information captured by available sensors (located on aerial and/or satellite platforms) has acquired a very important role in a wide range of human activities such as, for instance, management of environment and natural resources (including forests, water, geological and mineralogical resources), prevention of risks and catastrophes, planning of urban and rural spaces, detection of military objectives and intelligence tasks, among others. This has been fostered by the fact that a detailed characterization of the Earth's surface is now possible using data collected by current remote sensing instruments for EO, which are able to collect data with higher spatial and spectral resolutions, thus allowing for the acquisition of a large variety of remotely sensed images, from panchromatic and RGB data to multispectral and hyperspectral scenes, from LiDAR and radar sensors, to thermal and optical images, and from low to medium, high and very high spatial resolutions.For instance, the sensors capable of acquiring images with hundreds of spectral bands (called imaging spectrometers) are able to gather large amounts of information for the same area by recording hundreds of measurements in the spectral domain at different wavelengths. This allows to see what the human eye cannot, making possible the generation of data cubes, also known as hyperspectral images (HSI) with very large dimensionality. These images permit a very precise characterization of the terrestrial surface. For example, NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor is able to capture HSI scenes with 224 spectral bands between 0.4 and 2.5 micrometers, and spatial resolution of about 20 meters per pixel. Such wealth of spatial and spectral information (despite imposing important computational requirements) has opened new possibilities in many applications, including the detailed characterization of agricultural and urban areas, or the monitoring and prevention of natural disasters such as forest fires, oil spills and other types of chemical contamination.The goal of this thesis is the development and application of new image processing techniques for adequate and computationally efficient exploitation of remotely sensed HSI scenes, making use of the complementarity of spatial and spectral information available in the data, and exploring new computationally efficient models for extracting information from these remotely sensed images, with particular interest in the development of parallel and distributed techniques based on graphical processing units (GPUs) and cloud computing platforms. Our focus is on the development of new and efficient techniques for extracting information based on new trends in advanced learning algorithms (including deep learning techniques). For this purpose, we conduct experiments using a variety of HSI benchmark scenes that have been widely used in the community, illustrating the advantages of our newly developed algorithms with regards to other state-of-the-art techniques available in the literature.