  Citation: Tinega, H.C.; Chen, E.; Ma, L.; Nyasaka, D.O.; Mariita, R.M. HybridGBN-SR: A Deep 3D/2D Genome Graph-Based Network for Hyperspectral Image Classification. Remote Sens. 2022, 14, 1332. https://doi.org/10.3390/rs14061332 Academic Editors: Pedro Latorre-Carmona and Antonio J. Plaza Received: 22 February 2022 Accepted: 7 March 2022 Published: 9 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). remote sensing Article HybridGBN-SR: A Deep 3D/2D Genome Graph-Based Network for Hyperspectral Image Classification Haron C. Tinega 1 , Enqing Chen 1, *, Long Ma 1 , Divinah O. Nyasaka 2 and Richard M. Mariita 3 1 School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China; tinegaharon@gmail.com (H.C.T.); ielongma@zzu.edu.cn (L.M.) 2 The Kenya Forest Service, Nairobi P.O. Box 30513-00100, Kenya; dondieki@kenyaforestservice.org 3 Microbial BioSolutions, Troy, New York, NY 12180, USA; richard.mariita@microbialbiosolutions.com * Correspondence: ieeqchen@zzu.edu.cn; Tel.: +86-371-6778-1544 Abstract: The successful application of deep learning approaches in remote sensing image classifica- tion requires large hyperspectral image (HSI) datasets to learn discriminative spectral–spatial features simultaneously. To date, the HSI datasets available for image classification are relatively small to train deep learning methods. This study proposes a deep 3D/2D genome graph-based network (abbreviated as HybridGBN-SR) that is computationally efficient and not prone to overfitting even with extremely few training sample data. At the feature extraction level, the HybridGBN-SR utilizes the three-dimensional (3D) and two-dimensional (2D) Genoblocks trained using very few samples while improving HSI classification accuracy. The design of a Genoblock is based on a biological genome graph. From the experimental results, the study shows that our model achieves better classification accuracy than the compared state-of-the-art methods over the three publicly available HSI benchmarking datasets such as the Indian Pines (IP), the University of Pavia (UP), and the Salinas Scene (SA). For instance, using only 5% labeled data for training in IP, and 1% in UP and SA, the overall classification accuracy of the proposed HybridGBN-SR is 97.42%, 97.85%, and 99.34%, respectively, which is better than the compared state-of-the-art methods. Keywords: convolutional neural networks; genome graph; hyperspectral image classification; remote sensing; remote sensing image classification; residual learning; spectral–spatial features 1. Introduction Remote sensing works by moving a vision system (satellite or aircraft) across the Earth’s surface at various spatial resolutions and in different spectral bands of the magnetic spectrum to capture hyperspectral images (HSI) [1]. The vision system uses both imaging and spectroscopic methods to spatially locate specific components within the image scene under investigation based on their spectral features. The collected HSI data are a three- dimensional data structure with the x and y axes capturing the dimensions of the spatial images, and the z-axis is the number of spectral bands. Consequently, each pixel located on the x–y spatial domain contains a label representing the physical land cover of the target location [2]. For feature extraction and classification purposes, the voluminous spectral–spatial cues present in the HSI image represent an advantage in the detailed representation of the analyzed samples. However, they contain high spectral redundancy caused by sig- nificant interclass similarity and intraclass variability caused by changes in atmospheric, illumination, temporal, and environmental conditions, leading to data handling, stor- age, and analysis challenges [2]. For instance, an HSI system with a spatial resolution of 145 × 145 pixels will produce an image with 21,025 pixels for one spectral band. If the data contain 200 spectral bands, then a single image would produce over 4 million (145 × 145 × 200) data points. To overcome the challenges of spectral redundancy, most of Remote Sens. 2022, 14, 1332. https://doi.org/10.3390/rs14061332 https://www.mdpi.com/journal/remotesensing