{"title":"The Effect of Minimum Noise Fraction on Multispectral Imagery Data for Vegetation Canopy Density Modelling","authors":"A. M. Syarif, Ignatius Salivian Wisnu Kumara","doi":"10.14710/GEOPLANNING.5.2.251-258","DOIUrl":null,"url":null,"abstract":"Minimum Noise Fraction (MNF) is known as one of the method to minimize noise on hyperspectral imagery. In addition, there are not many studies have tried to show the effect of MNF transform on multispectral data. This study purposes to determine the effect of MNF transform on the accuracy level of vegetation density modeling using 10 meters Sentinel-2A spatial resolution (multispectral data) and to know the cause. The study area is located in parts of Sapporo City, Hokkaido, Japan. Vegetation density is modelled through vegetation index approach, Normalized Difference Vegetation Index (NDVI). The results show that the coefficient correlation of vegetation density data and vegetation index regression after MNF transformation (0.801623) has higher value than the same regression without the MNF (0.794481). However, better correlation does not represent the better accuracy on vegetation density modeling. Accuracy calculation through standard error of estimate shows the use of MNF in multispectral data for vegetation density modeling causes the decrease of model accuracy value. The accuracy of vegetation density model without involving MNF transformation reached 91.402 %, while the model accuracy through MNF transformation before vegetation density modeling reached 90.889 %. The insignificant increased accuracy is occurred due to the limited number of multispectral image information compared to hyperspectral image data. ","PeriodicalId":30789,"journal":{"name":"Geoplanning Journal of Geomatics and Planning","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/GEOPLANNING.5.2.251-258","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoplanning Journal of Geomatics and Planning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14710/GEOPLANNING.5.2.251-258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 2
Abstract
Minimum Noise Fraction (MNF) is known as one of the method to minimize noise on hyperspectral imagery. In addition, there are not many studies have tried to show the effect of MNF transform on multispectral data. This study purposes to determine the effect of MNF transform on the accuracy level of vegetation density modeling using 10 meters Sentinel-2A spatial resolution (multispectral data) and to know the cause. The study area is located in parts of Sapporo City, Hokkaido, Japan. Vegetation density is modelled through vegetation index approach, Normalized Difference Vegetation Index (NDVI). The results show that the coefficient correlation of vegetation density data and vegetation index regression after MNF transformation (0.801623) has higher value than the same regression without the MNF (0.794481). However, better correlation does not represent the better accuracy on vegetation density modeling. Accuracy calculation through standard error of estimate shows the use of MNF in multispectral data for vegetation density modeling causes the decrease of model accuracy value. The accuracy of vegetation density model without involving MNF transformation reached 91.402 %, while the model accuracy through MNF transformation before vegetation density modeling reached 90.889 %. The insignificant increased accuracy is occurred due to the limited number of multispectral image information compared to hyperspectral image data.
最小噪声分数(MNF)是实现高光谱图像噪声最小化的方法之一。此外,试图展示MNF变换对多光谱数据影响的研究并不多。本研究旨在确定MNF变换对10 m Sentinel-2A空间分辨率(多光谱数据)植被密度建模精度水平的影响,并了解其原因。研究区域位于日本北海道札幌市的部分地区。植被密度建模采用植被指数法,即归一化植被指数(NDVI)。结果表明,经过MNF变换后的植被密度数据与植被指数回归的相关系数(0.801623)高于未经过MNF变换的相同回归的相关系数(0.794481)。然而,相关性越好并不代表植被密度模型的精度越高。通过估计的标准误差进行精度计算表明,在多光谱数据中使用MNF进行植被密度建模导致模型精度值下降。未进行MNF变换的植被密度模型精度达到91.42%,而在植被密度建模前进行MNF变换的模型精度达到90.889%。与高光谱图像数据相比,由于多光谱图像信息数量有限,导致精度的提高不显著。