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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 1–25 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
FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite ImageryCode1
Reduction of Class Activation Uncertainty with Background InformationCode1
Classification and understanding of cloud structures via satellite images with EfficientUNetCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
SpinalNet: Deep Neural Network with Gradual InputCode1
2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips—0
Adversarial Examples in Remote Sensing—0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Morphological Network: How Far Can We Go with Morphological Neurons?—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
Satellite image classification with neural quantum kernels—0
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification—0
Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District—0
Improving Human-AI Collaboration With Descriptions of AI Behavior—0
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification—0
Diagnosing Model Performance Under Distribution ShiftCode0
Discriminative Learning of Deep Convolutional Feature Point DescriptorsCode0
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