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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 21–30 of 33 papers

TitleStatusHype
Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification—0
Diagnosing Model Performance Under Distribution ShiftCode0
Discriminative Learning of Deep Convolutional Feature Point DescriptorsCode0
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image ClassificationCode0
DeepSat - A Learning framework for Satellite ImageryCode0
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networksCode0
Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated DataCode0
Kolmogorov-Arnold Network for Satellite Image Classification in Remote SensingCode0
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