Remote sensing extraction of orchard in the casis of weigan and kuqa riv-ers based on Google earth engine
Aiming at the problems of difficult extraction and low recognition accuracy of orchards in arid areas,based on the Google earth engine(GEE)platform,this study comprehensively applied Sentinel-1/Sentinel-2 images to con-struct feature sets.By comparing the three optimization methods of original feature combination,Jeffries-Matusita(J-M)distance and attribute importance,combined with random forest(RF)classification method,the best optimization method was obtained,and the optimal classification feature set of orchard was explored.The results showed that the best recognition scheme was G17JM,the overall accuracy was 91.25%,kappa coefficient was 0.89,and area accuracy was 82.55%.The op-timal feature set was B8 asm,B8 ent,B8 idm,NDVIre3,B6,B7,a,e,b,EVI,B11,B8A,B8,VV.Using J-M dis-tance to optimize the feature set can effectively reduce the amount of data and improve the computational efficiency,which was more conducive to the accurate identification of orchard planting area.It shows that GEE is feasible to obtain orchard planting area quickly and accurately,and provides a strong basis for obtaining orchard dynamic changes.
Google earth engine(GEE)feature optimizationJ-M distancefeature set
万方数据
维普
