Discriminative feature distillation via correlation alignment and triplet constraints for image retrieval in wildfire scenarios

Accurate and efficient image retrieval is vital for wildfire monitoring and Natural Disaster Management (NDM). During an outbreak, new image data is captured and requires to be processed very fast to enable rapid localization of wildfire outbreaks, smoke plumes, and burned regions, as well as assessment of fire severity. However, existing retrieval systems rarely exploit spatial correlation information within image feature maps—an important cue for capturing fine-grained structural patterns in aerial and satellite imagery. In this work, we propose a correlation alignment knowledge distillation framework tailored for disaster image retrieval. Our approach transfers both semantic and structural knowledge from high-capacity teacher models—including large vision-language and self-supervised foundation models such as CLIP and DINO—to lightweight student networks optimized for deployment. By combining triplet-based metric learning with spatial correlation alignment, the proposed method produces discriminative and retrieval-effective features while significantly reducing DNN model complexity. We evaluate our approach on wildfire imagery and extended large-scale retrieval datasets that simulate real-world conditions, including class imbalance and high intra-class variability. Results demonstrate that our method maintains strong retrieval performance under resource-limited conditions, making it suitable for operational use in natural disaster response systems.