## 5 Application of the MAME-ZsL in Hyperspectral Image Analysis  
Advances in artificial intelligence, combined with the extended availability of high quality data and advances in both hardware and software, have led to serious developments in the efficient processing of data related to the GeoAI field (_Artificial Intelligence and Geography/Geographic Information Systems_). Hyperspectral Image Analysis for efficient and accurate object detection using Deep Learning is one of the timely topics of GeoAI. The most recent research examples include detection of soil characteristics [18], detailed ways of capturing densely populated areas [19], extracting information from scanned historical maps [20], semantic point sorting [21], innovative spatial interpolation methods [22] and traffic forecasting [23]. Similarly, modern applications of artificial vision and imaging (IMG) systems significantly extend the distinctive ability of optical systems, both in terms of spectral sensitivity and resolution. Thus, it is possible to identify and differentiate spectral and spatial regions, which although having the same color appearance, are characterized by different physico-chemical and/or structural properties. This differentiation is based on the emerging spatial diversification, which is detected by observing in narrow spectral bands, within or outside the visible spectrum. Recent technological developments have made it possible to combine IMG (spatial variation in RGB resolution) and spectroscopy (spectral analysis in spatially emitted radiation) in a new field called \"Spectral Imaging\" (SIM). In the SIM process, the intensity of light is recorded simultaneously as a function of both wavelength and position. The dataset corresponding to the observed surface contains a complete image, different for each wavelength. In the field of spectroscopy, a fully resolved spectrum can be recorded for each pixel of the spatial resolution of the observation field. The multitude of spectral regions, which the IMG system can manage, determines the difference between multispectral (tens of regions) and Hyperspectral (hundreds of regions) Imaging. The key element of a typical spectral IMG system is the monochromatic image sensor (monochrome camera), which can be used to select the desired observation wavelength. It can be easily perceived that the success of a sophisticated Hyperspectral Analysis System (HAS) is a major challenge for DL technologies, which are using a series of algorithms attempting to model data characterized by high-level of abstraction. HAS use a multi-level processing architecture, which is based on sequential linear and non-linear transformations. Despite their undoubtedly well-established and effective approaches and their advantages, these architectures depend on the performance of training with huge datasets which include multiple representations of images of the same class. Considering the multitude of classes which may be included in a Hyperspectral image, we realize that this process is so incredibly time consuming and costly, that it can sometimes be impossible to run [1]. The ZsL method was adopted based on a heuristic [24], hierarchical parameter search methodology [25]. It is part of a family of learning techniques which exploit data representations to interpret and derive the optimal result. This methodology uses distributed representation, the basic premise of which is that the observed data result from the interactions of factors which are organized in layers. A fundamental principle is that these layers correspond to levels of abstraction or composition based on their quantity and size. Fine-Grained Recognition (FIG_RC) is the task of distinguishing between visually very similar objects, such as identifying the species of a bird, the breed of a dog or the model of an aircraft. On the other hand, FIG_RC [26] which aims to identify the type of an object among a large number of subcategories, is an emerging application with the increasing resolution which exposes new details in image data.