Search Results for author:
Found 6 papers, 4 papers with code
Tree species classification from hyperspectral data using graph-regularized neural networks
We propose a novel graph-regularized neural network (GRNN) algorithm for tree species classification.
Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering
Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe.
Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images
Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials.
SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs
To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia.
Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
From these three-dimensional crowns, we are able to measure individual tree biomass.
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality.
