1 code implementation • 24 Dec 2023 • Kangning Cui, Ruoning Li, Sam L. Polk, Yinyi Lin, Hongsheng Zhang, James M. Murphy, Robert J. Plemmons, Raymond H. Chan
However, the high dimensionality, presence of noise and outliers, and the need for precise labels of HSIs present significant challenges to HSIs analysis, motivating the development of performant HSI clustering algorithms.
1 code implementation • 19 Jun 2022 • Kangning Cui, Seda Camalan, Ruoning Li, Victor P. Pauca, Sarra Alqahtani, Robert J. Plemmons, Miles Silman, Evan N. Dethier, David Lutz, Raymond H. Chan
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 of developing countries.
no code implementations • 28 Apr 2022 • Kangning Cui, Ruoning Li, Sam L. Polk, James M. Murphy, Robert J. Plemmons, Raymond H. Chan
DSIRC then locates high-density, high-purity pixels far in diffusion distance (a data-dependent distance metric) from other high-density, high-purity pixels and treats these as cluster exemplars, giving each a unique label.
no code implementations • 19 Apr 2022 • Sam L. Polk, Aland H. Y. Chan, Kangning Cui, Robert J. Plemmons, David A. Coomes, James M. Murphy
Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe.
1 code implementation • 13 Apr 2022 • Sam L. Polk, Kangning Cui, Robert J. Plemmons, James M. Murphy
Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms.
1 code implementation • 29 Mar 2022 • Ruoning Li, Kangning Cui, Raymond H. Chan, Robert J. Plemmons
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 full use of spatial and spectral information.
1 code implementation • 18 Mar 2022 • Sam L. Polk, Kangning Cui, Aland H. Y. Chan, David A. Coomes, Robert J. Plemmons, James M. Murphy
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.