OHID-1: A New Large Hyperspectral Image Dataset for Multi-Classification

Abstract

In the context of the increasing popularity of Big Data paradigms and deep learning techniques, we introduce a novel large-scale hyperspectral imagery dataset, termed Orbita Hyperspectral Images Dataset-1 (OHID-1). It comprises 10 hyperspectral images sourced from diverse regions of Zhuhai City, China, each boasting 32 spectral bands with a spatial resolution of 10 meters and spanning a spectral range of 400–1000 nanometers. The core objective of this dataset is to elevate the performance of hyperspectral image classification and pose substantial challenges to existing hyperspectral image processing algorithms. When compared to traditional open-source hyperspectral datasets and recently released large-scale hyperspectral datasets, OHID-1 presents more intricate features and a higher degree of classification complexity by providing 7 classes labels in wider area. Furthermore, this study demonstrates the utility of OHID-1 by testing it with selected hyperspectral classification algorithms. This dataset will be useful to advance cutting-edge research in urban sustainable development science, land use analysis. We invite the scientific community to devise novel methodologies for an in-depth analysis of these data.