HYPERSPECTRAL IMAGE CLASSIFICATION IN DESERT GRASSLAND BASED ON 3D DEEP LEARNING MODEL
DOI : https://doi.org/10.35633/inmateh-69-46
Identification and classification of vegetation are the basis for grassland degradation monitoring, classification and quantification studies. Here, four deep learning models were used to classify the unmanned aerial vehicle (UAV) hyperspectral remote sensing images of desert grassland. VGG16 and ResNet18 achieved better image classification results for vegetation and bare soil, whereas three-dimensional (3D)-VGG16 and 3D-ResNet18, improved by 3D convolutional kernels, achieved better classification for vegetation, bare soil and small sample features in the images. The number of convolutional kernels, its size and batch size parameters of each model were optimised, and 3D-ResNet18-J had the best classification performance, with an overall classification accuracy of 97.74%. It achieved high precision and efficiency in classifying UAV hyperspectral remote sensing images of desert grassland.
Abstract in Chinese