基于GF-1和Landsat 9卫星影像融合的农作物分类OA

[Purposes]In this study,the possibility of improving classification accuracy for crop type identification is examined with data fusion technology.[Methods]The GF-1 and Landsat 9 im-ages were used to perform data fusion for crop type classification in Jinzhong region of Shanxi Prov-ince,China.The combination of PC Spectral Sharpening(PC),Gram-Schmidt Pan Sharpening(GS),and NNDiffuse Pan Sharpening(NN)fusion models facilitated the integration of the red,green,blue,and near-infrared bands from GF-1 WFV and Landsat 9 satellite images.Assessment of fusion outcomes according to mean,standard deviation,and information entropy identified the optimal fusion bands.By employing the Random Forest classification algorithm,crop classification was conducted on GF-1 WFV images,Landsat 9 images,and the best-fused images.[Results]Results demonstrate a significant enhancement in crop classification accuracy and stability for the fused GF-1 and Landsat 9 images,achieving an overall classification accuracy of 92.9%,a Kappa coefficient of 0.92,and an F1 Score of 87.4%.Furthermore,the overall accuracy,Kappa coefficient,and F1 Score of crop classifi-cation in the fused image are increased by 1.7%,0.2,and 0.6%,respectively,compared with classifi-cation solely based on the GF-1 WFV image.Similarly,compared with Landsat 9 image classifica-tion,improvements are 3.2%,0.4,and 4.4%,respectively.The utilization of GF-1 WFV near-infrared band and application of the NN algorithm to fuse Landsat 9 data demonstrate promising re-sults in crop classification,highlighting its potential for widespread utilization in accurately extracting agricultural information across extensive geographical areas.