Spatial-Spectral Cross-Correlation Embedded Dual-Transfer Network for Object Tracking Using Hyperspectral Videos

Hyperspectral (HS) videos can describe objects at the material level due to their rich spectral bands, which are more conducive to object tracking compared with color videos. However, the existing HS object trackers cannot make good use of deep-learning models to mine their semantic information due to limited annotation data samples. Moreover, the high-dimensional characteristics of HS videos makes the training of a deep-learning model challenging. To address the above problems, this paper proposes a spatial-spectral cross-correlation embedded dual-transfer network (SSDT-Net). Specifically, first, we propose to use transfer learning to transfer the knowledge of traditional color videos to the HS tracking task and develop a dual-transfer strategy to gauge the similarity between the source and target domain. In addition, a spectral weighted fusion method is introduced to obtain the inputs of the Siamese network, and we propose a spatial-spectral cross-correlation module to better embed the spatial and material information between the two branches of the Siamese network for classification and regression. The experimental results demonstrate that, compared to the state of the art, the proposed SSDT-Net tracker offers more satisfactory performance based on a similar speed to the traditional color trackers.

Automated summary

This paper proposes a spatial-spectral cross-correlation embedded dual-transfer network (SSDT-Net) for hyperspectral video object tracking, addressing the challenges of limited annotation data and high-dimensional characteristics. Experimental results show that the SSDT-Net offers satisfactory performance with a similar speed to traditional color trackers.