CAJ | 학술논문

由于高维特征空间通常会导致不适定问题，针对高光谱影像的统计模式识别是非常艰巨的任务。随着波段数目的增加，高光谱影像分析则面临Hughes现象等障碍，因此促进了降维方法的发展，它能够有效处理有限训练样本下的高维数据集情形。降维算法的目标是在保持原始数据主要本征信息的同时获取高维数据样本的低维表示。为了能够有效解决高光谱影像分析中的“维数灾难”问题，从而改进后续计算复杂度，我们引入一种半监督局部保持的降维算法。
유우고유특정공간통상회도치불괄정문제，침대고광보영상적통계모식식별시비상간거적임무。수착파단수목적증가，고광보영상분석칙면림Hughes현상등장애，인차촉진료강유방법적발전，타능구유효처리유한훈련양본하적고유수거집정형。강유산법적목표시재보지원시수거주요본정신식적동시획취고유수거양본적저유표시。위료능구유효해결고광보영상분석중적“유수재난”문제，종이개진후속계산복잡도，아문인입일충반감독국부보지적강유산법。
Statistical pattern-classification of hyperspectral imagery (HSI) is a difficult endeavor, due to the high-dimensional fea?ture spaces often tend to result in ill-conditioned problem. Obstacles, such as the hughes phenomenon, arise as the data dimension?ality increases, thus fostering the development of dimension reduction methods, which are able to deal with high-dimensional data sets and limited training samples. The goal of dimension reduction is to obtain a low-dimensional representation of high-dimen?sional data samples while preserving most of the 'intrinsic information' contained in the original data. In order to effectively solve the problem of 'dimension disaster', thereby improving the computational complexity, we introduce a new semi-supervised local discriminant analysis algorithm.