Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine With Weighted Composite Features for Spectral-Spatial Classification of Hyperspectral Images
Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine With Weighted Composite Features for Spectral-Spatial Classification of Hyperspectral Images
复制标题
基于稀疏表示的增强多项式 Logistic 极限学习机，具有加权复合特征，用于高光谱图像的光谱空间分类
DOI：
10.1109/tgrs.2018.2828601
复制
发表时间：
2018-11-01
影响因子：
8.2
通讯作者：
Benedilasson, Jon Atli
中科院分区：
文献类型：
--
作者：
Cao, Faxian;Yang, Zhijing;Benedilasson, Jon Atli
尽管极限学习机（ELM）已成功应用于许多模式识别问题，但由于两个主要缺点，仅使用原始的ELM很难对高光谱图像（HSI）的分类产生高精度。首先是由于随机生成的初始权重和偏差，无法保证ELM的最优输出。第二个是分类器中缺乏空间信息，因为传统的 ELM 仅利用光谱信息对 HSI 进行分类。为了解决这两个问题，提出了一种基于 ELM 的 HSI 光谱空间分类的新框架，其中采用稀疏表示和加权复合特征（WCF）的概率建模来导出优化的输出权重并提取空间特征。首先，ELM 在使用最大后验估计量的统计建模下表示为凹对数似然函数。其次，将稀疏表示应用于拉普拉斯先验，以有效地确定具有唯一最大值的对数后验，以解决 ELM 的不适定问题。随后使用变量分裂和增强拉格朗日来进一步降低所提出算法的计算复杂度。第三，使用WCF提取空间信息来构建光谱空间分类框架。此外，所提出方法的下界是通过严格的数学证明得出的。三个公开可用的 HSI 数据集的实验结果表明，所提出的方法优于 ELM 以及许多最先进的方法。
Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main drawbacks. The first is due to the randomly generated initial weights and bias, which cannot guarantee optimal output of ELM. The second is the lack of spatial information in the classifier as the conventional ELM only utilizes spectral information for classification of HSI. To tackle these two problems, a new framework for ELM-based spectral–spatial classification of HSI is proposed, where probabilistic modeling with sparse representation and weighted composite features (WCFs) is employed to derive the optimized output weights and extract spatial features. First, ELM is represented as a concave logarithmic-likelihood function under statistical modeling using the maximum a posteriori estimator. Second, sparse representation is applied to the Laplacian prior to efficiently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm. Third, the spatial information is extracted using the WCFs to construct the spectral–spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on three publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and also a number of state-of-the-art approaches.