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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
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image ClassificationCode1
Reduction of Class Activation Uncertainty with Background InformationCode1
FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite ImageryCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
Classification and understanding of cloud structures via satellite images with EfficientUNetCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Adversarial Examples in Remote Sensing—0
2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips—0
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