UC-HSI: UAV-Based Crop Hyperspectral Imaging Datasets and Machine Learning Benchmark Results

Accurate and timely information about crop types and their distribution is crucial for effective agricultural management, resource allocation, and policy-making. Remote sensing (RS) imaging has the potential to automate the identification and mapping of crops over large areas. The low spatial resolutions of satellite-based platforms and the limited spectral capability of RGB/multispectral cameras are the bottlenecks in efficient crop categorization. Unmanned aerial vehicle (UAV)-based hyperspectral imaging (HSI) technology has the potential to classify/map agricultural landscapes efficiently due to its extensive coverages, rich spectral information, and high spatial and temporal resolutions. However, very few crop hyperspectral (HS) datasets are publicly available, on which the research community relies on for developing and testing algorithms, which are created from either in situ spectral measurements or low-resolution satellite images. This letter presents UAV-borne Crop HyperSpectral Image (UC-HSI) datasets in the 385–1021-nm spectral range. The detailed steps for creating clean HSI datasets from UAV-based HS images are described. We also proposed a novel convolutional transformer fusion architecture to classify the crop HSI datasets efficiently and compared its performance with machine learning (ML) benchmarks; it obtained the best accuracy of 95.26% in categorizing ten crop varieties. The datasets and codes will be made publicly available at
https://github.com/sankaraug/CrHyperS
to benefit the RS community, allowing them to develop and test algorithms on UAV-based HSI data.