Vector-guided vehicle detection from high-resolution satellite imagery

Abstract

Recent availability of high-resolution satellite imagery provides a new data source to extract small-scale objects such as vehicles. Seldom has vehicle detection been applied to high-resolution satellite imagery where panchromatic band resolutions are presently in the range of 0.6-1.0 m. With the limited spatial resolution, reliable vehicle detection can only be achieved by incorporating contextual information. Here we use a GIS road vector map to guide vehicle detection by restricting the search to the road network. A morphological shared-weight neural network (MSNN) is used to classify image pixels on roads into targets and nontargets. Then a target aim point selection algorithm is designed to locate vehicle centroids. The MSNN has been successfully used for automatic target recognition (ATR) from multiple sensors. In this paper, we present experimental results of MSNN vehicle detection applied to 1 m IKONOS panchromatic data. A vehicle image base library is built by collecting more than 300 cars manually from 16 road segments in 3 test images. To suppress false alarms, a screening algorithm is developed using morphological filtering to identify vehicle pixels and nontarget pixels that are similar to vehicle pixels. Subimages centered at those pixels are used as positive and negative training samples of the MSNN. The MSNN was trained on subimage samples from 11 road segments. The performance results on the training segments and remaining 5 validation segments are reported. The MSNN shows a good generalization behavior. After learning, the detection rate exceeds 85% with very few false alarms