*Article* **GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification**

### **Konstantinos Demertzis \* and Lazaros Iliadis**

Department of Civil Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece; liliadis@civil.duth.gr

**\*** Correspondence: kdemertz@fmenr.duth.gr; Tel.: +30-694-824-1881

Received: 13 February 2020; Accepted: 4 March 2020; Published: 7 March 2020

**Abstract:** Deep learning architectures are the most e ffective methods for analyzing and classifying Ultra-Spectral Images (USI). However, e ffective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the *Model-Agnostic Meta-Ensemble Zero-shot Learning* (MAME-ZsL) approach. The MAME-ZsL overcomes the above di fficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a *Zero-shot Learning* (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it o ffers an improved training stability, high generalization performance and remarkable classification accuracy.

**Keywords:** model-agnostic meta-learning; ensemble learning; GIS; hyperspectral images; deep learning; remote sensing; scene classification; geospatial data; Zero-shot Learning
