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Found 8 papers, 4 papers with code
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets
Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem.
DeepSat - A Learning framework for Satellite Imagery
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
A Theoretical Analysis of Deep Neural Networks for Texture Classification
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.
Core Sampling Framework for Pixel Classification
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.
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images.
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters
Classification techniques for images of handwritten characters are susceptible to noise.
Ranked #1 on
Document Image Classification
on n-MNIST
From Satellite Imagery to Disaster Insights
The use of satellite imagery has become increasingly popular for disaster monitoring and response.
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
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
