基于LFDA和GA-ELM的高光谱图像地物识别方法研究

高光谱图像的高维特性和波段间的高相关性，导致高光谱图像地物识别问题研究中，面临着数据量大、信息冗余的问题，降低了高光谱图像的分类识别精度。针对以上问题，提出了基于局部保留降维（Local Fisher Discriminant Analysis，LFDA）结合遗传算法（Genetic Algorithm， GA ）优化极限学习机（Extreme Learning Machine， ELM）的高光谱图像分类方法。首先，采用LFDA对高光谱图像数据进行降维处理，消除信息冗余并保留局部邻域内主要特征；然后用GA优化ELM，对降维处理后的特征样本进行分类，提高高光谱图像的分类识别精度。将该方法应用于Salinas和Pavia University高光谱图像的地物识别问题研究，分类精度分别达到了98.56%和97.11%，由此验证了该方法的有效性。

Research on Feature Recognition Method of Hyperspectral Image based on LFDA and GA-ELM

The high-dimensional characteristics of the hyperspectral image and the high correlation between the bands have led to the problem of large data volume and information redundancy in the study of the feature recognition of hyperspectral images， which reduces the classification and recognition accuracy of hyperspectral images. Aiming at the above problems， a hyperspectral image classification method based on Local Fisher Discriminant Analysis （LFDA） combined with Genetic Algorithm （GA） to optimize Extreme Learning Machine （ELM） is proposed. First， the LFDA is used to reduce the dimensionality of the hyperspectral image data to eliminate information redundancy and retain the main features in the local neighborhood； then use GA to optimize the ELM， classify the feature samples after the dimensionality reduction， and improve the classification and recognition of the hyperspectral image Precision. The method proposed in this paper is applied to the research on the feature recognition of hyperspectral images in Salinas and Pavia University. The classification accuracy reaches 98.56% and 97.11% respectively， which verifies the effectiveness of the method in this paper.