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Found 7 papers, 3 papers with code
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
Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
Machine learning has proven to be useful in classification and segmentation of images.
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes.
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants.
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.
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
