Design of adaptive feature extraction algorithm based on fuzzy classifier in hyperspectral imagery classification for big data analysis
Design of adaptive feature extraction algorithm based on fuzzy classifier in hyperspectral imagery classification for big data analysis
复制标题
大数据分析高光谱图像分类中基于模糊分类器的自适应特征提取算法设计
DOI：
10.1109/wcica.2016.7578527
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
P. Behera
中科院分区：
文献类型：
--
作者：
Juan F. Ramirez Rochac;N. Zhang;P. Behera
我们提出了一种新的自适应特征提取（FEA）的方法，集成了每像素/字段分类和光谱混合的概念。它结合了它们在自适应特征选择方面的优势，同时最大限度地减少了与每种技术的高复杂性相关的缺点。该方法包括局部梯度计算，参考类的确定，原型分类使用模糊分类器，和特征向量的选择。使用由123个样本和1254个特征组成的模拟高光谱立方体进行多个实验，并且仅出于验证目的进行分类。交叉验证表明，与全特征分析相比，FEA在误分类错误上平均提高了7%。
We proposed a new adaptive feature extraction (FEA) approach that integrates concepts of per-pixel/field classification and spectral ummixing. It combines their advantages in adaptive feature selection while minimizing the disadvantages associated with the high-complexity of each technique. The approach consists of local gradients calculation, reference clusters determination, prototype classification using fuzzy classifier, and feature vectors selection. Multiple experiments were performed using a simulated hyperspectral cube composed by 123 samples and 1254 features and classification was done only for verification purposes. Cross-validation demonstrated that FEA generated an average improvement of 7% on the misclassification error when compared to full feature analysis.