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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

Showing 110 of 33 papers

TitleStatusHype
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
Satellite image classification with neural quantum kernels0
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT ModelsCode0
Kolmogorov-Arnold Network for Satellite Image Classification in Remote SensingCode0
An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image ClassificationCode0
FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite ImageryCode1
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance0
Reduction of Class Activation Uncertainty with Background InformationCode1
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