Deep Feature-Based Hyperspectral Object Tracking: An Experimental Survey and Outlook

With the rapid advancement of hyperspectral imaging technology, hyperspectral object tracking (HOT) has become a research hotspot in the field of remote sensing. Advanced HOT methods have been continuously proposed and validated on scarce datasets in recent years, which can be roughly divided into handcrafted feature-based methods and deep feature-based methods. Compared with methods via handcrafted features, deep feature-based methods can extract highly discriminative semantic features from hyperspectral images (HSIs) and achieve excellent tracking performance, making them more favored by the hyperspectral tracking community. However, deep feature-based HOT still faces challenges such as data-hungry, band gap, low tracking efficiency, etc. Therefore, it is necessary to conduct a thorough review of current trackers and unresolved problems in the HOT field. In this survey, we systematically classify and conduct a comprehensive analysis of 13 state-of-the-art deep feature-based hyperspectral trackers. First, we classify and analyze the trackers based on the framework and tracking process. Second, the trackers are compared and analyzed in terms of tracking accuracy and speed on two datasets for cross-validation. Finally, we design a specialized experiment for small object tracking (SOT) to further validate the tracking performance. Through in-depth investigation, the advantages and weaknesses of current HOT technology based on deep features are clearly demonstrated, which also points out the directions for future development.