CAJ | 학술논문

光谱解混分析的重要研究内容是计算分析各地物类别成分在混合像素内所占的比例技术。文中以实测高光谱数据为研究对象，针对高光谱数据具有高维度数、严重的光谱混合等特点，基于流形学习中局部线性嵌入(LLE)算法的思想，提出了一种约束最小乘方局部线性加权回归(CLS-LLWR)建模方法。通过4种典型地物的光谱吸收特征差异分析，从它们不同比例组合下的实测混合光谱中选取了不同波段范围，分别对该模型预测覆盖度信息能力进行了验证分析。最后，将CLS-LLWR模型与主成分回归(PCR)和偏最小二乘回归(PLSR)模型,通过计算预测标准误差(SE)进行了对比分析。结果表明，CLS-LLWR模型有较好的预测能力。这为流形学习在高光谱遥感图像信息提取方面进行了有意的探索。
광보해혼분석적중요연구내용시계산분석각지물유별성분재혼합상소내소점적비례기술。문중이실측고광보수거위연구대상，침대고광보수거구유고유도수、엄중적광보혼합등특점，기우류형학습중국부선성감입(LLE)산법적사상，제출료일충약속최소승방국부선성가권회귀(CLS-LLWR)건모방법。통과4충전형지물적광보흡수특정차이분석，종타문불동비례조합하적실측혼합광보중선취료불동파단범위，분별대해모형예측복개도신식능력진행료험증분석。최후，장CLS-LLWR모형여주성분회귀(PCR)화편최소이승회귀(PLSR)모형,통과계산예측표준오차(SE)진행료대비분석。결과표명，CLS-LLWR모형유교호적예측능력。저위류형학습재고광보요감도상신식제취방면진행료유의적탐색。
The main study on spectral unmixing is to develop a regression between mixed spectral features of main land-cover types and their responding fractional cover. Studying on in situ spectral reflectance data, based on one of the best known algorithms of manifold learning, locally linear embedding (LLE), a new modeling method named constrained least squares locally linear weighted regression (CLS-LLWR) was proposed. Spectral reflectance of four kinds of the mixed land-cover types in different percentages was measured and preliminarily analyzed. The model CLS-LLWR was verified by predicting fractional cover of main land- cover types. Compared with principal component regression (PCR) and partial least squares regression (PLSR), through comparison and analysis of the standard error of prediction(SE), the result shows that the CLS-LLWR has better predictability. This study indicates that manifold study has the potential for the information extraction of mixed land cover types in hyperspectral image.