Anhui General Rural Economic Information Center

Anhui, China

Anhui General Rural Economic Information Center

Anhui, China
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Zhao J.,Anhui University | Guo J.,Anhui University | Cheng W.,Anhui General Rural Economic Information Center | Xu C.,Anhui University | Huang L.,Anhui University
Modern Physics Letters B | Year: 2017

A cross-comparison method was used to assess the SPOT-6 optical satellite imagery against Chinese GF-1 imagery using three types of indicators: spectral and color quality, fusion effect and identification potential. More specifically, spectral response function (SRF) curves were used to compare the two imagery, showing that the SRF curve shape of SPOT-6 is more like a rectangle compared to GF-1 in blue, green, red and near-infrared bands. NNDiffuse image fusion algorithm was used to evaluate the capability of information conservation in comparison with wavelet transform (WT) and principal component (PC) algorithms. The results show that NNDiffuse fused image has extremely similar entropy vales than original image (1.849 versus 1.852) and better color quality. In addition, the object-oriented classification toolset (ENVI EX) was used to identify greenlands for comparing the effect of self-fusion image of SPOT-6 and inter-fusion image between SPOT-6 and GF-1 based on the NNDiffuse algorithm. The overall accuracy is 97.27% and 76.88%, respectively, showing that self-fused image of SPOT-6 has better identification capability. © 2017 World Scientific Publishing Company


Huang L.-S.,Anhui University | Huang L.-S.,Anhui General Rural Economic Information Center | Ju S.-C.,Anhui University | Ju S.-C.,Anhui General Rural Economic Information Center | And 12 more authors.
International Journal of Agriculture and Biology | Year: 2015

yellow rust (Puccinia striiformis f. sp. tritici) on winter wheat (Triticum aestivum l.) has resulted in significant reductions in the yield losses and wheat grain quality. It is extremely important to quantitatively detect and assess such a serious disease rather than visual qualitative description. In comparison with traditional diagnosis method, remote sensing has proven to be a cost-effective tool to achieve such a goal. In this study, we used yellow rust in winter wheat to illustrate the capability of estimating the infection index in different leaf layers of the plant using hyperspectral measurements on individual wheat diseased leaves. the analysis results indicated that the severities showed a gradual increasing trend from F-1 (F=Flag leaf) to F-3, while the relative chlorophyll and nitrogen showed an inverse change. the spectral reflectance gradually increased from F-1 to F-3 in the visible and short-wave infrared (SWIR) regions, while it was the very reverse in the near-infrared (NIR) region. In addition, an integral spectral index - yellow rust spectral index (YRSI) was constructed to quantitatively estimate the disease using the most sensitive bands in the visible (704 nm), NIR (1423 nm) and SWIR (1926 nm) regions. The coefficient of determination (R2) reaches 0.88 between the disease severity (DS%) and YRSI, which shows that the index can be suitable and effective to estimate the infection severity for a wheat plant. © 2015 Friends Science Publishers.

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