  Thermal images have various applications in security, medical and industrial
domains. This paper proposes a practical deep-learning approach for thermal
image classification. Accurate and efficient classification of thermal images
poses a significant challenge across various fields due to the complex image
content and the scarcity of annotated datasets. This work uses a convolutional
neural network (CNN) architecture, specifically ResNet-50 and VGGNet-19, to
extract features from thermal images. This work also applied Kalman filter on
thermal input images for image denoising. The experimental results demonstrate
the effectiveness of the proposed approach in terms of accuracy and efficiency.
