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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
Satellite Image Classification with Deep Learning—0
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification—0
DeepSat V2: Feature Augmented Convolutional Neural Nets for 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 District—0
Morphological Network: How Far Can We Go with Morphological Neurons?—0
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning—0
Adversarial Examples in Remote Sensing—0
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
Discriminative Learning of Deep Convolutional Feature Point DescriptorsCode0
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