Satellite Image Classification
7 papers with code • 3 benchmarks • 5 datasets
Satellite image classification is the most significant technique used in remote sensing for the computerized study and pattern recognition of satellite information, which is based on diversity structures of the image that involve rigorous validation of the training samples depending on the used classification algorithm.
Benchmarks
These leaderboards are used to track progress in Satellite Image Classification
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Datasets
WorldStrat
Cross-View Time Dataset
NASA Worldview
UV6K
Most implemented papers
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO).
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.
Discriminative Learning of Deep Convolutional Feature Point Descriptors
etrulls/deepdesc-release
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ICCV 2015
Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.
An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
mabdelhack/hyperspectral_image_generator
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Tensorflow tool allows for rapid prototyping and testing of deep learning models, however, its built-in image generator is designed to handle a maximum of four spectral channels.
Classification and understanding of cloud structures via satellite images with EfficientUNet
Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years.
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
A different classifier from the generator and the discriminator is used in the 3D-HyperGAMO model, which is trained using both original and generated samples to {determine} the classes of newly generated samples to which they actually belong.
Diagnosing Model Performance Under Distribution Shift
In order to do this, we define a hypothetical distribution on $X$ consisting of values common in both training and target, over which it is easy to compare $Y \mid X$ and thus predictive performance.
