论文标题
遥感中降维方法的分析和合成仪表板
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing
论文作者
论文摘要
高光谱图像（HSI）分类是高技术遥感软件。目的是复制主题图。 HSI包含相关区域的一百多个高光谱度量，作为带（或简单的图像）。它们是在邻居的频率上占据的。不幸的是，有些频段是冗余特征，其他频段是嘈杂的测量，并且具有使分类精度较差的特征的高维度。有问题的是如何找到良好的乐队来对区域项目进行分类。一些方法使用共同信息（MI）和阈值来选择相关图像，而无需处理冗余。其他人控制并避免冗余。但是他们处理降低维度的缩小，有时是选择，而其他时间则是包装方法，而没有任何关系。在这里，我们介绍了一项有关所用方案的调查，在批评和改进之后，我们合成了一个仪表板，该仪表板有助于用户分析假设的功能选择和提取软件。
Hyperspectral images (HSI) classification is a high technical remote sensing software. The purpose is to reproduce a thematic map . The HSI contains more than a hundred hyperspectral measures, as bands (or simply images), of the concerned region. They are taken at neighbors frequencies. Unfortunately, some bands are redundant features, others are noisily measured, and the high dimensionality of features made classification accuracy poor. The problematic is how to find the good bands to classify the regions items. Some methods use Mutual Information (MI) and thresholding, to select relevant images, without processing redundancy. Others control and avoid redundancy. But they process the dimensionality reduction, some times as selection, other times as wrapper methods without any relationship . Here , we introduce a survey on all scheme used, and after critics and improvement, we synthesize a dashboard, that helps user to analyze an hypothesize features selection and extraction softwares.
