根据噪声和带宽条件，智能筛选多光谱图像关键波段，提升分类准确率。
Conditional Optimal Filter Selection for Multispectral Object Classification
- 基于噪声与带宽条件动态筛选最优波段，减少冗余。
- 在SMM数据集上将误分类数从318降至124。
- 适合需要低成本硬件的医学、农业等多光谱应用。
使用多光谱相机阵列捕获图像在医疗、农业和环境监测中日益重要。然而，使用所有可用波段不切实际且产生大量数据，而实际任务仅需部分波段。相邻波段常携带相似信息，因此评估过程应剔除冗余波段以降低复杂度和数据负载。现有方法通常预设固定数量的波段进行选择。本文提出一种新方法，引入噪声水平和滤光片带宽等预设条件，最小化波段冗余。此外，可实现最小滤光片配置，在保持低硬件成本的同时获取全部关键光谱信息。相比快速二分搜索法，在随机森林分类器下，SMM数据集的误分类对象数从318降至124。
原文摘要 · Abstract (English)
Capturing images using multispectral camera arrays has gained importance in medical, agricultural and environmental processes. However, using all available spectral bands is infeasible and produces much data, while only a fraction is needed for a given task. Nearby bands may contain similar information, therefore redundant spectral bands should not be considered in the evaluation process to keep complexity and the data load low. In current methods, a restricted and pre-determined number of spectral bands is selected. Our approach improves this procedure by including preset conditions such as noise or the bandwidth of available filters, minimizing spectral redundancy. Furthermore, a minimal filter selection can be conducted, keeping the hardware setup at low costs, while still obtaining all important spectral information. In comparison to the fast binary search filter band selection method, we managed to reduce the amount of misclassified objects of the SMM dataset from 318 to 124 using a random forest classifier.
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