SOTAVerified

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

Showing 1120 of 33 papers

TitleStatusHype
Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated DataCode0
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT ModelsCode0
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
DeepSat - A Learning framework for Satellite ImageryCode0
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image ClassificationCode0
An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image ClassificationCode0
Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District0
Adversarial Examples in Remote Sensing0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning0
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