极限学习机在高光谱遥感图像分类中的应用

摘要

In view of hyperspectral remote sensing image classification, this paper introduces Limit learning theory and proposes a novel classification approach for a hyperspectral image (HSI) using a hierarchical local receptive field (LRF) based extreme learning machine (ELM). Considering the local correlations of spectral features, hierarchical architectures with two layers can potentially extract abstract representation and invariant features for better classification performance. Simultaneously, the influence of different parameters of the algorithm on classification performance is also analyzed. Experimental results on two widely used real hyperspectral data sets confirm that the comparison with the current some advanced methods, and the proposed HSI classification approach has faster training speed and better classification performance.%针对高光谱遥感图像的分类问题,本文引入极限学习的思想,提出了基于分层局部感受野的极限学习机的高光谱分类方法.该方法利用光谱特征的局部相关性,采用两层的分层结构提取高光谱图像中的抽象表示和不变特征,可以取得更好的分类性能.同时还分析了算法的不同参数对分类性能的影响.在两个广泛使用的真实高光谱数据集上进行实验,同当前一些典型的方法做比较,结果表明该方法具有更高的分类性能与较快的训练速度.