  This study presents an advanced Convolutional Neural Network (CNN)
architecture for ship classification from optical satellite imagery,
significantly enhancing performance through the integration of the
Convolutional Block Attention Module (CBAM) and additional architectural
innovations. Building upon the foundational ResNet50 model, we first
incorporated a standard CBAM to direct the model's focus towards more
informative features, achieving an accuracy of 87% compared to the baseline
ResNet50's 85%. Further augmentations involved multi-scale feature integration,
depthwise separable convolutions, and dilated convolutions, culminating in the
Enhanced ResNet Model with Improved CBAM. This model demonstrated a remarkable
accuracy of 95%, with precision, recall, and f1-scores all witnessing
substantial improvements across various ship classes. The bulk carrier and oil
tanker classes, in particular, showcased nearly perfect precision and recall
rates, underscoring the model's enhanced capability in accurately identifying
and classifying ships. Attention heatmap analyses further validated the
improved model's efficacy, revealing a more focused attention on relevant ship
features, regardless of background complexities. These findings underscore the
potential of integrating attention mechanisms and architectural innovations in
CNNs for high-resolution satellite imagery classification. The study navigates
through the challenges of class imbalance and computational costs, proposing
future directions towards scalability and adaptability in new or rare ship type
recognition. This research lays a groundwork for the application of advanced
deep learning techniques in the domain of remote sensing, offering insights
into scalable and efficient satellite image classification.
