多光谱物体分类的层次化滤光片波段选择
Hierarchical Filter Band Selection for Multispectral Object Classification
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中文总结 AI 辅助
本文提出一种层次化波段选择方法，通过引入材质估计的第二分类阶段并结合决策树策略，在SMM50数据集上实现分类误差相对降低28.9%，并将所需相机数从7台减至4台。
中文摘要 AI 辅助
多光谱相机阵列在各种光谱波段捕获图像数据，使得图像采集能够超越人类感知范围。这些系统广泛应用于医疗、农业、环境和遥感领域。然而，并非所有记录的波段都适用于分类任务，因此减少波段数量可以降低硬件复杂性和成本。条件滤光片波段选择算法通过选择低噪声、非冗余的波段来最小化滤光片和相机的数量，从而解决这一问题。本文通过引入第二个分类阶段来改进该方法，该阶段在物体标签之外还估计物体材质。这一信息通过基于决策树的波段选择策略进行融合。所提出的方法在SMM50数据集上相比现有最先进技术实现了分类误差相对降低28.9%。此外，在相同分类精度下，所需相机数量从7台减少到4台，表明所提方法在提升性能的同时显著降低了硬件需求。
英文摘要
Multispectral camera arrays capture image data in various spectral bands, enabling image acquisition beyond human perception. These systems are widely used in medical, agricultural, environmental, and remote sensing applications. However, not all recorded bands are needed for classification tasks, thus reducing them can lower hardware complexity and cost. The conditional filter band selection algorithm addresses this by selecting low-noise, non-redundant bands to minimize the number of filters and cameras. This paper improves the approach by introducing a second classification stage that estimates object material in addition to the object label. This information is merged by a decision-tree based band selection strategy. The proposed method achieves a 28.9% relative reduction in classification error on the SMM50 dataset compared to the state-of-the-art. Moreover, for the same classification accuracy, the required number of cameras is reduced from 7 to 4, demonstrating that the proposed approach improves performance while significantly lowering hardware requirements.
