联合改进拉普拉斯特征映射和k-近邻分类器的高光谱影像分类

Hyperspectral Imagery Classification Using the Combination of Improved Laplacian Eigenmaps and Improved k-nearest Neighbor Classifier

高光谱影像利用流形学习降维和分类器分类时往往忽略了影像本身的空间特征,这将严重制约最终的分类精度。因此,本文以拉普拉斯特征映射和k-近邻分类器为例,提出了自适应加权综合核距离来同时改进流形学习方法和分类器方法,目的在于改善高光谱影像的分类结果。自适应加权综合核距离同时考虑高光谱影像的光谱特征和空间特征,且能够针对每个像素点自动估算空间邻域来描述空间特征。通过Indian和PaviaU两个数据集来分析和验证本文提出的组合策略,实验结果表明,本文提出的组合策略得到的分类结果明显优于常规拉普拉斯特征映射降维和常规k-近邻分类的组合策略,能够得到更高精度的分类结果。

Hyperspectral imagery （HSI） classification can be achieved through the combination scheme of nonlinear dimensionality reduction using Laplacian eigenmaps （LE） and classification using the knearest neighbor （KNN） classifier. However, both the LE and the KNN classifier omit spatial features of HSI data as the imagery. That seriously restricts the classification result of HSI data. This paper presents the adaptive weighted summation kernel distance （AWSKD） to improve both the LE and the KNN classifier, aiming to promote the classification accuracies of HSI data. The AWSKD considers the spectral and spatial features of HSI data, and adaptively estimate the proper spatial neighborhood size for describing the spatial feature of each pixel. The Indian and PaviaU datasets are utilized to analyze and testify the proposed combination scheme of improved LE （ILE） and improved KNN （IKNN） classifier. Experimental results show that the proposed combination scheme achieves sharply higher classification accuracies than the regular scheme of LE and KNN.