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A Transformer-Based Network for Hyperspectral Object Tracking
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AI
DOI：10.1109/TGRS.2023.3325049.png)
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摘要
En 中文
With the abundant spectral information, the hyperspectral images could be benefit for tracking the target in various application scenarios. Most of the predominant hyperspectral object tracking methods were based on transferred red-green-blue (RGB) object tracking networks, since the lack of training data. Different strategies of processing the hyperspectral images to adapt the transferred RGB object tracking networks have been exploited. However, the existing strategies led to lose the spectral information or the interaction information between bands and have shown limited performances. In this article, a novel transformer-based hyperspectral object tracking algorithm (Trans-HST) is proposed to make advantages of the spectral information with transformer modules. In Trans-HST, the cross-band groups of feature enhancement (CBFE) is introduced to reduce the negative effects of the interaction information loss. To address the problem of spectral information loss, the transformer-based deep features' fusion (TDFF) fuses the deep features corresponding to different groups of bands in the hyperspectral images and integrates the deep feature corresponding to the original hyperspectral images into the fused features. Experiments on the commonly used hyperspectral object tracking dataset have been applied to verify the effectiveness of the two proposed modules, and they indicate the superior performance of the Trans-HST comparing with other RGB and hyperspectral trackers.
Keyword:
Target tracking
Feature extraction
Object tracking
Transformers
Hyperspectral imaging
Fuses
Deep learning
Feature enhancement
feature fusion
hyperspectral object tracking
transformer
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
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