2 citations · 4 across the 5 of their papers we have counts for
5 papers
Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering
Kangning Cui, Ruoning Li, Sam L. Polk +5
Hyperspectral images (HSIs) provide exceptional spatial and spectral resolution of a scene, crucial for various remote sensing applications. However, the high dimensionality, prese…
Semi-supervised Change Detection of Small Water Bodies Using RGB and Multispectral Images in Peruvian Rainforests
Kangning Cui, Seda Camalan, Ruoning Li +7
Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests…
Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry
Kangning Cui, Ruoning Li, Sam L. Polk +3
Hyperspectral images, which store a hundred or more spectral bands of reflectance, have become an important data source in natural and social sciences. Hyperspectral images are oft…
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification
Raymond H. Chan, Ruoning Li
Hyperspectral images often have hundreds of spectral bands of different wavelengths captured by aircraft or satellites that record land coverage. Identifying detailed classes of pi…
Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation
Ruoning Li, Kangning Cui, Raymond H. Chan +1
In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify hyperspectral images, which makes…