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论文名称：CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

发表刊物：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

摘要：Visual object tracking is a fundamental task in computer vision and thrived in recent decades. With the development
of snapshot hyperspectral (HS) sensors, efforts have been made
to exploit tracking the object with HS videos to overcome the
inherent limitation of red–green–blue (RGB) images. Existing
HS tracking algorithms extract the deep features from image
data separately, which break the interaction information between
bands. Therefore, the discrimination ability of HS trackers is limited
and the efficiency of the existing HS algorithms is low. In this
article, a novel algorithm [cross-ban

第一作者：高隆

论文类型：期刊论文

是否译文：否