DNN Object Detection in High-Resolution Multispectral Satellite Imagery with Varied Bit-Depth and Spectral Bands

While the availability of multispectral imagery (MSI) is increasing, there is still much to be learned regarding the spectral sensitivity of deep neural networks (DNNs). This work presents and analyzes DNNs trained to perform object detection in high-resolution multispectral, satellite imagery. These DNNs are first trained and evaluated on variations of the xView dataset with varied bit-depths and spectral band inclusion. Inference is also performed on test imagery with different bit-depths or with missing spectral information in order to analyze their importance for object detection. Initial experiments show an MSI-trained DNN had an average optimal F1-score 3.7 points higher than the best performing RGB network. Additional experiments suggest significant DNN sensitivity to changes in bit-depth and an even greater sensitivity to the loss of any given spectral band. The results given here highlight the need for future research regarding spectral biases of DNNs for object detection in high-resolution MSI.