70 citations · 133 across the 6 of their papers we have counts for
7 papers
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
Qun Liu, Saikat Basu, Sangram Ganguly +4
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent i…
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters
Manohar Karki, Qun Liu, Robert DiBiano +2
Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this pap…
CactusNets: Layer Applicability as a Metric for Transfer Learning
Edward Collier, Robert DiBiano, Supratik Mukhopadhyay
Deep neural networks trained over large datasets learn features that are both generic to the whole dataset, and specific to individual classes in the dataset. Learned features tend…
Core Sampling Framework for Pixel Classification
Manohar Karki, Robert DiBiano, Saikat Basu +1
The intermediate map responses of a Convolutional Neural Network (CNN) contain information about an image that can be used to extract contextual knowledge about it. In this paper,…
A Theoretical Analysis of Deep Neural Networks for Texture Classification
Saikat Basu, Manohar Karki, Robert DiBiano +4
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative represe…
DeepSat - A Learning framework for Satellite Imagery
Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay +3
Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent i…