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Satellite Image Classification

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.

Papers

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Showing 1–10 of 33 papers

TitleStatusHype
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image ClassificationCode2
FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite ImageryCode1
Reduction of Class Activation Uncertainty with Background InformationCode1
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image ClassificationCode1
Classification and understanding of cloud structures via satellite images with EfficientUNetCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networksCode0
Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera DataCode0
Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated DataCode0
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