no code implementations • 25 May 2023 • Akash Awasthi, Son Ly, Jaer Nizam, Samira Zare, Videet Mehta, Safwan Ahmed, Keshav Shah, Ramakrishna Nemani, Saurabh Prasad, Hien Van Nguyen
The definition of anomaly detection is the identification of an unexpected event.
1 code implementation • 12 Oct 2020 • Thomas Vandal, Daniel McDuff, Weile Wang, Andrew Michaelis, Ramakrishna Nemani
These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another.
no code implementations • 13 Jun 2020 • Nicholas Gao, Max Wilson, Thomas Vandal, Walter Vinci, Ramakrishna Nemani, Eleanor Rieffel
Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes.
1 code implementation • 15 Nov 2019 • Qun Liu, Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert DiBiano, Manohar Karki, Ramakrishna Nemani
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning.
Ranked #1 on Satellite Image Classification on SAT-4
1 code implementation • 29 Oct 2019 • Kate Duffy, Thomas Vandal, Weile Wang, Ramakrishna Nemani, Auroop R. Ganguly
A difficult test for deep learning-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner.
no code implementations • 28 Jul 2019 • Thomas Vandal, Ramakrishna Nemani
Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the trade-offs to spatial, spectral and temporal resolutions of observations.
no code implementations • 12 Feb 2019 • Edward Collier, Kate Duffy, Sangram Ganguly, Geri Madanguit, Subodh Kalia, Gayaka Shreekant, Ramakrishna Nemani, Andrew Michaelis, Shuang Li, Auroop Ganguly, Supratik Mukhopadhyay
Machine learning has proven to be useful in classification and segmentation of images.
1 code implementation • 13 Feb 2018 • Thomas Vandal, Evan Kodra, Jennifer Dy, Sangram Ganguly, Ramakrishna Nemani, Auroop R. Ganguly
Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes.
no code implementations • 9 Mar 2017 • Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, Auroop R. Ganguly
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants.
no code implementations • 9 May 2016 • Saikat Basu, Manohar Karki, Robert DiBiano, Supratik Mukhopadhyay, Sangram Ganguly, Ramakrishna Nemani, Shreekant Gayaka
To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate.
1 code implementation • 11 Sep 2015 • Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert DiBiano, Manohar Karki, Ramakrishna Nemani
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning.
Ranked #2 on Satellite Image Classification on SAT-6
no code implementations • 11 Sep 2015 • Saikat Basu, Manohar Karki, Sangram Ganguly, Robert DiBiano, Supratik Mukhopadhyay, Ramakrishna Nemani
Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem.