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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
Morphological Network: How Far Can We Go with Morphological Neurons?—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification—0
Out-of-distribution detection in satellite image classification—0
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning—0
Recurrent Neural Networks to Correct Satellite Image Classification Maps—0
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance—0
Satellite image classification and segmentation using non-additive entropy—0
Satellite image classification methods and Landsat 5TM bands—0
Satellite Image Classification with Deep Learning—0
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