Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks

High-resolution satellite imagery has recently become a new data source for extraction of small-scale objects such as vehicles. Very little vehicle detection research has been done using high-resolution satellite imagery where panchromatic band resolutions are presently in the range of 0.6–1.0 m. Given the limited spatial resolution, reliable vehicle detection can only be achieved by incorporating contextual information. Here, a GIS road vector map is used to constrain a vehicle detection system to road networks. We used a morphological shared-weight neural network (MSNN) to learn an implicit vehicle model and classify pixels into vehicles and non-vehicles. A vehicle image base library was built by collecting more than 300 cars manually from test images. Strategies to reduce the false alarms and select target centroids were designed. Experimental results indicate that the MSNN performed very well. The detection rate on both training and validation sites exceeded 85% with very few false alarms. By learning the implicit vehicle model through a MSNN, our method outperforms a baseline blob detection method.