Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification.

Area of Science:

• Remote Sensing
• Computer Vision
• Machine Learning

Background:

• Deep learning, particularly Convolutional Neural Networks (CNNs), excels at feature extraction from hyperspectral images.
• Current CNN training methods often overlook spectral variability and statistical properties within hyperspectral data classes.
• Imbalanced and limited training samples introduce uncertainty in conventional sample-based penalization methods.

Purpose of the Study:

• To develop a novel statistical loss function for deep learning in hyperspectral image analysis.
• To address the limitations of sample-based penalization by incorporating statistical distributions of data classes.
• To enhance the discrimination capabilities of deep learning models for hyperspectral image classification.

Main Methods:

• Characterizing each hyperspectral image class as a statistical distribution.
• Developing a statistical loss function based on Fisher's discrimination criterion to penalize intraclass variance.
• Incorporating a diversity-promoting condition to increase interclass variance.
• Utilizing multivariant statistical analysis for the statistical estimation of the loss function.

Main Results:

• The proposed statistical loss effectively penalizes intraclass variance, reducing uncertainty caused by limited training data.
• The diversity-promoting condition enhances interclass variance, leading to better discrimination between different classes.
• Experimental results on real-world hyperspectral images demonstrate the effectiveness of the statistical loss function.

Conclusions:

• The novel statistical loss function improves deep learning performance for hyperspectral image classification.
• By modeling class distributions and optimizing variance, the method enhances model robustness and accuracy.
• This approach offers a more statistically grounded alternative to traditional sample-based loss functions in hyperspectral imaging.