Feature extraction in hyperspectral imaging using adaptive feature selection approach
Feature extraction in hyperspectral imaging using adaptive feature selection approach
复制标题
使用自适应特征选择方法进行高光谱成像特征提取
DOI：
10.1109/icaci.2016.7449799
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
本文提出了一种新的自适应特征选择技术的设计和实现的光谱波段选择遥感高光谱图像分类之前。该方法以自适应的方式集成了光谱波段选择和高光谱图像分类，最终目标是改善高光谱成像的分析和解释。提出的自适应特征选择的四个组成部分，包括局部梯度计算，参考聚类确定，使用模糊分类器的原型类建设，和相关波段的选择。使用ROSIS（反射光学系统成像光谱仪）的高光谱图像数据集作为训练和测试数据。我们测试了不同数量的选定的光谱带的方法的效果。用ROC曲线说明AFS的分类准确性。此外，为了将所提出的方法与其他方法进行比较，我们将所提出的自适应特征选择（AFS）方法和主成分分析（PCA）方法应用于处理ROSIS Pavia场景后使用不同数量的光谱带的GentleBoost分类器。实验结果表明，AFS方法的分类精度高于PCA方法。此外，对于每种方法，光谱波段数越高，分类精度越高。
This paper presents the design and implementation of a new adaptive feature selection technique for spectral band selection prior to classification of remotely sensed hyperspectral images. This approach integrates spectral band selection and hyperspectral image classification in an adaptive fashion, with the ultimate goal of improving the analysis and interpretation of hyperspectral imaging. The four components in the proposed adaptive feature selection, including local gradient calculation, reference cluster determination, prototype classes building using a fuzzy classifier, and relevant bands selection are presented in detail. The hyperspectral image data set from the ROSIS (Reflective Optics System Imaging Spectrometer) were used as training and testing data. We tested the effect of the approach on different number of selected spectral bands. The classification accuracy for AFS was illustrated by the ROC curve. In addition, in order to compare the proposed method with other methods, we applied the proposed adaptive feature selection (AFS) approach and the principal component analysis (PCA) method to the GentleBoost classifier using different number of spectral bands after processing the ROSIS Pavia scene. The experimental results demonstrated that the classification accuracies obtained by the AFS method are higher than that of the PCA method. In addition, for each method, the higher the number of spectral bands, the higher the classification accuracy.