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	分数阶微分在盐渍土高光谱数据预处理中的应用
其他题名	Application of fractional differential in preprocessing hyperspectral data of saline soil
	张东; 塔西甫拉提·特依拜; 张飞; 阿尔达克·克里木
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2014
卷号	30 期号:24 页码:151-160
中文摘要	光谱微分技术在高光谱数据处理中应用广泛,为研究分数阶微分对光谱反射率与盐渍土含盐量之间相关系数的影响,细化相关系数变化趋势,该文选取新疆塔里木南缘于田绿洲盐渍土为研究对象,以土壤样本含盐量和反射率高光谱数据为数据源,利用Grunwald-Letnikov分数阶微分公式编程计算光谱反射率以及对应的均方根、倒数、对数、对数倒数、倒数对数变换的0~2阶微分(间隔0.2阶),对比分析每种变换各阶微分与土壤含盐量相关系数曲线的变化趋势以及微分处理对单波段相关系数的影响。结果表明:经微分处理,通过相关系数0.01显著性检验水平的波段数量明显增加(0.6阶>1阶>2阶>0阶),随着阶数增加,呈现先增后减的趋势,且均在分数阶0.6处达到最多。在0.6阶处,光谱反射率及5种数学变换通过相关系数0.01显著性检验的波段数量按照从大到小为:倒数对数变换=对数变换>均方根变换>倒数变换>光谱反射率>对数倒数变换。对于波段2444、2423、2142、2005 nm,微分算法能够大幅提升与含盐量之间的相关性,相关系数绝对值取最大值对应的阶数均为分数阶。从局部到整体,分数阶微分提升相关性的效果明显优于整数阶微分。该研究结果为分数阶微分在高光谱技术监测土壤盐渍化现象中的应用提供参考依据。
英文摘要	Soil salinization is not only one of the most serious environmental problems in semi-arid and arid area, but it also leads to land degradation and productivity loss. At present, most studies on soil salinization pay much attention to the quantifying of the relationship between the saline soil salt content and integer differential transform of hyperspectral data. The integer differential method only focuses on the points in differential windows, thus if extending the integer calculus to fractional order, more information could be discovered due to the advantages of fractional differential method: it has memory and nonlocality. In this paper, the authors took the Delta oasis of Yutian in the southern rim of Tarim Basin in Xinjiang as the study area, and measured the spectral reflectance and the soil salt content in order to obtain the degrees of salinity in the study area. Firstly, the hyperspectral reflectance data were treated with 5 kinds of mathematical transform: root mean square, inversion, logarithm, inversion-logarithm, and logarithm-inversion. Secondly we calculated their 0-2nd order (interval 0.2-order) derivative by using the Grunwald-Letnikov fractional order differential formula and Java programming language, then computed the correlation coefficient between the salt content and the data of each mathematical transform and each order differential. Subsequently, we comparatively analyzed the varying trend between correlation coefficient curves and the influence of correlation coefficient on single bands treated by the differential method. The results showed that differentials could evidently increase the number of the bands highly significantly correlated with salt content (0.6-order>first-order>second-order>0-order), the number followed increasing-decreasing trend (reaching the maximum at 0.6-order) with the increase of differential order. For spectral reflectance and other mathematical transform at 0.6-order, the numbers of the bands followed the order inversion-logarithm=logarithm>root mean square>inversion>spectral reflectance>logarithm-inversion. For the bands 2 444, 2 423, 2 142, and 2 005 nm, differential algorithm could significantly improve the correlation between salt content and spectra (and other mathematical transforms) of salinized soil, and all the maximum absolute values of correlation coefficient were obtained at the fractional order, corresponding to 0.6-order (logarithm-inversion transform corresponding to 0.4-order), 0.6-order (inversion transform corresponding to 0.8-order), 0.8-order, and 1.4-order respectively. In conclusion, from local to global, fractional differential had a better capacity than integer differential in lifting correlation. As the order increased, the correlation coefficient curves showed a gradual changing trend, and to some extent, capturing this trend could prevent information loss caused by big differences among the spectral reflectance, first-order, and second-order differential transform. We suggest here that further researches should be concentrated on physical meaning of fractional differential in hyperspectral data to provide theoretical basis to build and describe soil salinization quantitative inversion models.
中文关键词	土壤 ; 光谱分析 ; 数据处理 ; 盐渍化 ; 分数阶微分 ; 高光谱
英文关键词	soils spectrum analysis data processing salinization fractional differential hyperspectral
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:5327022
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/231503
作者单位	新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 新疆 830046, 中国
推荐引用方式 GB/T 7714	张东,塔西甫拉提·特依拜,张飞,等. 分数阶微分在盐渍土高光谱数据预处理中的应用[J]. 新疆大学,2014,30(24):151-160.
APA	张东,塔西甫拉提·特依拜,张飞,&阿尔达克·克里木.(2014).分数阶微分在盐渍土高光谱数据预处理中的应用.农业工程学报,30(24),151-160.
MLA	张东,et al."分数阶微分在盐渍土高光谱数据预处理中的应用".农业工程学报 30.24(2014):151-160.