Fusion of multispectral and stereo information for unsupervised target detection in VHR airborne data

Very high resolution multispectral imaging reached a high level of reliability and accuracy for target detection and classification. However, in an urban scene, the complexity is raised, making the detection and the identification of small objects difficult. One way to overcome this difficulty is to combine spectral information with 3D data. A set of (very high resolution) airborne multispectral image sequences was acquired over the urban area of Zeebrugge, Belgium. The data consist of three bands in the visible (VIS) region, one band in the near infrared (NIR) range and two bands in the mid-wave infrared (MWIR) region. Images are obtained images at a frame rate of 1/2 frame per second for the VIS and NIR image and 2 frames per second for the MWIR bands. The sensors have a decimetric spatial resolution. The combination of frame rate with flight altitude and speed results in a large overlap between successive images. The current paper proposes a scheme to combine 3D information from along-track stereo, exploiting the overlap between images on one hand and spectral information on the other hand for unsupervised detection of targets. For the extraction of 3D information, the disparity map between different image pairs is determined automatically using an MRF-based method. For the unsupervised target detection, an anomaly detection algorithm is applied. Different methods for inserting the obtained 3D information into the target detection scheme are discussed.