Основные понятия
Data augmentation techniques in remote sensing images must maintain physical consistency to improve model performance.
Аннотация
Introduction
Data augmentation crucial for DL methods in RS image classification.
Channel transformations debated for their impact on spectral data.
Methodology
Approach to measure expected deviation of pixel signatures.
Comparison between original and augmented pixel signatures.
Experimental Results
Evaluation on BigEarthNet-S2 dataset with various channel augmentation techniques.
Scores show the impact of brightness and grayscale adjustments on physical consistency.
Conclusion and Discussion
Proposed approach provides insights into the effects of channel augmentation techniques.
Findings suggest that maintaining physical consistency is vital for model improvement.
Статистика
"The training set contains 28226 images."
"Each validation and test set consists of 12013 images."
"We set the size of the sub-area k × k to 5 × 5."
Цитаты
"Data augmentation refers to the process of creating slightly modified versions of existing training images by applying stochastic transformations while preserving their semantic characteristics."
"Our results show that contrast, Gaussian blur, Gaussian noise, posterize, sharpness, and solarize do not affect the physical consistency of the signatures."