Gaussian Information Entropy based band Reduction for Unsupervised Hyperspectral Video Tracking

Abstract

Hyperspectral videos, which provide extra spectral characteristics besides spatial and temporal information, can improve the performance of object tracking using spectral signatures. However, there is a lack of labeled hyperspectral videos to support deep learning based model design. On the contrary, object tracking in the color space has been well developed in the past decade with many benchmark tracking models, e.g., SiamBAN. Therefore, how to transfer models designed in the color space to the hyperspectral space is of great importance. In this paper, hyperspectral videos are reduced into 3 bands using a band reduction algorithm, by which the existing well-trained trackers can be directly used. Specifically, Gaussian Information Entropy (GIE) is used to transform a hyperspectral video into a 3-band pseudo-color video, by which hyperspectral object tracking is conducted in an unsupervised mode. Experimental results demonstrate that object trackers designed in the color space can be transferred to hyperspectral videos using band reduction algorithms and the GIE based reduction is more effective than several well-known band reduction algorithms when using SiamBAN.