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Satellite Image Classification
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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.
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Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated Data
Satellite image classification with neural quantum kernels
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing
An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image Classification
FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance
Reduction of Class Activation Uncertainty with Background Information
Diagnosing Model Performance Under Distribution Shift
Improving Human-AI Collaboration With Descriptions of AI Behavior
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning
2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips
Out-of-distribution detection in satellite image classification
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Satellite Image Classification with Deep Learning
Classification and understanding of cloud structures via satellite images with EfficientUNet
SpinalNet: Deep Neural Network with Gradual Input
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification
An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image Classification
Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District
Morphological Network: How Far Can We Go with Morphological Neurons?
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
Adversarial Examples in Remote Sensing
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
Recurrent Neural Networks to Correct Satellite Image Classification Maps
DeepSat - A Learning framework for Satellite Imagery
Satellite image classification and segmentation using non-additive entropy
Satellite image classification methods and Landsat 5TM bands
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