Automated recognition and detection of dismounts and vehicles using close-in urban hyperspectral images

Abstract

Advances in Hyperspectral imaging (HSI) sensor offer new avenues for precise detection, identification and characterization of materials or targets of military interest. HSI technologies are capable of exploiting 10s to 100s of images of a scene collected at contiguous or selective spectral bands to seek out mission-critical objects. In this paper, we develop and analyze several HSI algorithms for detection, recognition and tracking of dismounts, vehicles and other objects. Preliminary work on detection, classification and fingerprinting of dismount, vehicle and UAV has been performed using visible band HSI data. The results indicate improved performance with HSI when compared to traditional EO processing. All the detection and classification results reported in this paper were based on <i>single</i> HSI pixel used for testing. Furthermore, the close-in Hyperspectral data were collected for the experiments at indoor or outdoor by the authors. The collections were taken in different lighting conditions using a visible HSI sensor. The algorithms studied for performance comparison include PCA, Linear Discriminant Analysis method (LDA), Quadratic classifier and Fisher's Linear Discriminant and comprehensive results have been included in terms of confusion matrices and Receiver Operating Characteristic (ROC) curves.