Multilevel data fusion for the detection of targets using multispectral image sequences

This paper presents an approach to the long range automatic detection of vehicles, using multi-sensor image sequences. The method was tested on a database of multi-spectral image sequences, acquired under diverse operational conditions. The approach consists of two parts. The first part uses a semi-supervised approach, based on texture parameters, for detecting stationary targets. For each type of sensor one learning image is chosen. Texture parameters are calculated at each pixel of the learning images and combined using logistic regression into a value that represents the conditional probability that the pixel belongs to a target given the texture parameters. The actual detection algorithm applies the same combination to the texture features calculated on the remainder of the database (test images). When the results of this feature level fusion are stored as an image, the local maxima correspond to likely target positions. These feature-level-fused images are calculated for each sens...