基于波段子集特征提取的最小二乘支持向量机高光谱图像分类技术

Classification Technique for Hyperspectral Image Based on Subspace of Bands Feature Extraction and LS-SVM

针对高光谱图像分类,文章提出一种基于波段子集最大噪声分量特征提取的最小二乘支持向量机的高光谱图像分类算法。利用高光谱图像的谱间相关性将原始光谱波段划分为若干个波段子集,并在各个子集上采用最大噪声分量方法进行特征提取,将提取的特征合成为分类的组合特征矢量,避免了高光谱图像较强的波段相关性,减少了谱间冗余。并且采用了最小二乘支持向量机,用等式约束取代了支持向量机中的不等式约束,降低了运算量,提高了学习效率。该方法利用特征提取优化了光谱信息,降低了谱间噪声,提高了分类器的性能。实验结果证明了本文算法的优越性。

The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM（least squares support vector machine）.The LS-SVM uses the features extracted from subspace of bands（SOB）.The maximum noise fraction（MNF） method is adopted as the feature extraction method.The spectral correlations of the hyperspectral image are used in order to divide the feature space into several SOBs.Then the MNF is used to extract characteristic features of the SOBs.The extracted features are combined into the feature vector for classification.So the strong bands correlation is avoided and the spectral redundancies are reduced.The LS-SVM classifier is adopted,which replaces inequality constraints in SVM by equality constraints.So the computation consumption is reduced and the learning performance is improved.The proposed method optimizes spectral information by feature extraction and reduces the spectral noise.The classifier performance is improved.Experimental results show the superiorities of the proposed algorithm.