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通过空间光谱注意力超频谱目标跟踪重量变量梯度和深度对比增强

Yao Yu^1,2, Mingkai Ge², Jie Yu²

¹School of Integrated Circuit Science and Engineering, Wuxi University, Wuxi 214105, China.

Sensors (Basel, Switzerland)

|February 27, 2026

概括

本研究引入了一种新的超光谱目标追踪方法,使用空间光谱注意力和深度估计来克服尺度变化. 这种方法提高了跟踪的稳定性,并实现了最先进的性能.

科学领域:

计算机视觉计算机视觉
遥感遥感遥感遥感
信号处理信号处理

背景情况:

超光谱目标跟踪由于尺度变化而面临重大挑战.
当前的方法在目标尺度动态变化时,难以保持跟踪精度.
强大的外观建模对于复杂环境中的有效跟踪至关重要.

研究的目的:

提出一种新的超光谱目标追踪方法,能够稳定地应对尺度变化.
通过将空间光谱注意力机制与深度估计相结合来提高追踪性能.
开发一种适应性融合战略,以提高追踪精度.

主要方法:

利用空间-光谱注意力重量变化梯度来减少维度和融合注意力重量.
实现了双路径预处理模块和视觉变压器编码器,具有深度对比增强.
使用重量自适应混合融合来结合注意力重量和深度信息.
嵌入深度感知几何约束和光谱空间信息,用于外观建模.

主要成果:

在超光谱视频序列上实现了最先进的性能.
与现有方法相比,在规模变化方面表现出卓越的稳定性.
实现了0.6704的曲线下面积 (AUC) 和0.9455.20的20%假阳性 (DP@20) 的检测精度.
在尺度变化的稳定性方面,性能比最先进的方法优于3.1%.

结论:

拟议的方法有效地解决了超光谱目标跟踪中的尺度变化.
整合空间光谱注意力和深度估计显著提高了跟踪稳定性.
深度感知方法为未来的超光谱跟踪研究提供了一个有希望的方向.

关键词:

视觉变压器视觉变压器深度对比增强增强深度对比超光谱视频视频的使用规模变化的变化.空间光谱注意力重量变异梯度.目标追踪目标追踪

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