期刊论文

中文

植物保护

0529-1542

2016

42

5

47-52

10.3969/j.issn.0529-1542.2016.05.007

国家林业公益性行业科研专项

本校为第一机构

利用美国Spectra Vista Corporation(以下均用简称SVC)HR-1024i非成像高光谱仪采集不同病情程度的降香黄檀冠层光谱数据,并结合地面同步调查获得的降香黄檀黑痣病病情指数数据,对光谱数据进行重叠校正(scan matching/overlap correction)和白光板反射率校正(white plate reflectance correction)。采用主成分分析法(PCA法)对与降香黄檀黑痣病病情指数相关性较高的敏感波段进行降维。利用53个训练集,将敏感波段和PCA法处理后的敏感波段分别作为输入变量,训练降香黄檀黑痣病的BP神经网络。两种输入变量建立的神经网络计算出的预测值与实际值之间的决定系数(R~2)均达到99%。利用27个验证集做进一步...

Canopy spectral data of Dalbergia odorifera were collected according to different disease incidences, using Spectra Vista Corporation (SVC) HR-1024i un-imaging hyperspectral of America, then scan matching/overlap correction and white plate reflectance correction of spectral data were completed based on the disease index of D. odorifera black scurf obtained simultaneously in the field. Principal component analysis (PCA) was applied to conduct dimension-reduction of sensitive wave band which highly related to disease index. Both sensitive wave bands from 53 training sets before and after process...