Reference clusters based feature extraction approach for mixed spectral signatures with dimensionality disparity
Reference clusters based feature extraction approach for mixed spectral signatures with dimensionality disparity
复制标题
基于参考簇的具有维数差异的混合光谱特征的特征提取方法
DOI：
10.1109/syscon.2016.7490576
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
N. Zhang
中科院分区：
文献类型：
--
作者：
J. Rochac;N. Zhang
本文解决了痕迹检测中的两个主要挑战：a）维度差异，即，高光谱立方体的高维度与训练数据集的小尺寸之间的不利比率，以及B）混合光谱特征，即，由于分辨率不足而存在嵌入在单个像素中的若干材料物质。在这种情况下，传统的纯基于像素的图像处理技术可能不适用，它仍然是学术界，政府，工业和其他非政府组织之间进行更多变革性研究的开放领域。为了解决这些问题，我们提出了一种新的特征提取方法（FEA）之前的监督分类的高光谱立方体的基础上，局部和全球的空间光谱分析。有限元分析调整其参数，以不同程度的混合物使用局部梯度和参考集群。自适应特征选择（AFE）的方法选择的光谱带的最小数目，而不损失的鉴别能力。我们测试了不同数量的选定光谱带的效果。具有两个光谱带的AFE给出了分类准确性，其中给出了曲线下面积（AUC）。
This paper addressed two main challenges in trace detection: a) dimensionality disparity, i.e., the unfavorable ratio between the high dimensionality of the hyperspectral cube and the small size of the training data set, and b) mixed spectral signatures, i.e., the presence of several material substances embedded in one single pixel due to an insufficient resolution. Under these circumstances, traditional pure pixel-based image processing techniques may not be applicable and it remains an open area for more transformative research among academia, government, industry and other non-government organizations. To address these issues, we proposed a new feature extraction approach (FEA) prior to supervised classification of hyperspectral cubes based on local and global spatio-spectral analysis. FEA adapted its parameters to different levels of mixtures using both local gradients and reference clusters. The adaptive feature selection (AFE) approach selected the minimum number of spectral bands without losing discriminating power. We tested the effect of different number of selected spectral bands. The AFE with two spectral bands gave classification accuracy, in which the area under the curve (AUC) was given.