Detection of Camouflage-Covered Military Objects Using High-Resolution Multi-Spectral Satellite Imagery

Here we evaluated the effectiveness of high-resolution multi-spectral (MS) satellite imagery for detection of Camouflage-Covered Military Objects (CCMO) using deep neural networks (DNN). We first divided the eight MS bands from the WorldView 2 & 3 commercial satellites into three separate 3-band MS partitions and then evaluated a variety of DNN models these partitions. The best DNN model using a single 3-band MS partition achieved an F1 score of 84.2% for CCMO detection. This was an 7.7% increase over the best baseline RGB DNN model that had an F1 score of 76.5% for CCMO detection. We then evaluated a variety of techniques to fuse multiple DNN model outputs from the same model architecture to further improve CCMO detection. The best DNN fusion technique improved the F1 score to 91% which is an increase of 14.4% over the best baseline RGB DNN model. Thus, the preliminary results from this study demonstrate significant potential for improving DNN detection for very challenging CCMO objects using the additional information available from high-resolution multi-spectral satellite image bands.