## 1 Introduction  
Hyperspectral image analysis and classification is a timely special field of Geoinformatics which has attracted much attention recently. This has led to the development of a wide variety of new approaches, exploiting both spatial and spectral content of images, in order to optimally classify them into discrete components related to specific standards. Typical information products obtained by the above approaches are related to diverse areas; namely: ground cover maps for environmental Remote Sensing; surface mineral maps used in geological applications; vegetation species maps, employed in agricultural-geoscience studies and in urban mapping. Recent developments in optical sensor technology and Geoinformatics (GINF), provide multispectral, Hyperspectral (HyS) and panchromatic images at very high spatial resolution. Accurate and effective HyS image analysis and classification is one of the key applications which can enable the development of new decision support systems. They can provide significant opportunities for business, science and engineering in particular. Automatic assignment of a specific semantic label to each object of a HyS image (according to its content) is one of the most difficult problems of GINF Remote Sensing (RES). With the available HyS resolution, subtle objects and materials can be extracted by HyS imaging sensors with very narrow diagnostic spectral bands. This can be achieved for a variety of purposes, such as detection, urban planning, agriculture, identification, surveillance and quantification. HyS image analysis enables the characterization of objects of interest (e.g., land cover classes) with unprecedented accuracy, and keeps inventories up to date. Improvements in spectral resolution have called for advances in signal processing and exploitation algorithms. A Hyperspectral image is a 3D data cube, which contains two-dimensional spatial information (image feature) and one-dimensional spectral information (spectral bands). Especially, the spectral bands occupy very fine wavelengths. Additionally, image features related to land cover and shape disclose the disparity and association among adjacent pixels from different directions at a confident wavelength. This is due to its vital applications in the design and management of soil resources, precision farming, complex ecosystem/habitat monitoring, biodiversity conservation, disaster logging, traffic control and urban mapping. It is well known that increasing data dimensionality and high redundancy between features might cause problems during data analysis. There are many significant challenges which need to be addressed when performing HyS image classification. Primarily, supervised classification faces a challenge related to the imbalance between high dimensionality and incomplete accessibility of training samples, or to the presence of mixed pixels in the data. Further, it is desirable to integrate the essential spatial and spectral information, so as to combine the complementary features which stem from source images. Deep Learning methodologies have significantly contributed towards the evolution and development of HyS image analysis and classification [1]. Deep Learning (DL) is a branch of computational intelligence which uses a series of algorithms that model high-level abstraction data using a multi-level processing architecture. It is difficult for all Deep Learning algorithms to achieve satisfactory classification results with limited labeled samples, despite their undoubtedly well-established functions and their advantages. The approaches with the highest classification accuracy and generalization ability fall under the supervised learning umbrella. For this reason, especially in the case of Ultra-Spectral Images, huge datasets with hundreds or thousands of specimens labeled by experts are required [2]. This process is very expensive and time consuming. In the case of supervised image classification, the input image is processed by a series of operations performed at different neuronal levels.