The object-oriented land use classification incorporating auxiliary data sets
Land use classification is critical to the management of land space.To improve the accuracy of land use classification,this study takes Bohu County as the research area,uses Sentinel-2A images to extract spectral features,and combines radar,spectral index,soil,and terrain features to construct six object-oriented land use classification models.We then use a simple non-iterative clustering algorithm and random forest algorithm to segment and classify the images and obtain the classification accuracy and feature importance ranking of the model.In the final step,we use the classification regression tree algorithm to verify the influence of the auxiliary dataset on the improvement of the classification accu-racy.The results show that when using the SNIC algorithm to segment the images,with seed size 17 and compactness 0,the image segmentation effect in this study area is the best.The classification accu-racy is the lowest when only spectral information is used,and adding any auxiliary dataset of radar,spectral index,soil,and terrain features can improve the classification accuracy of land use.Among those auxiliary datasets,the effect of terrain features on improving classification accuracy is more sig-nificant,and the classification accuracy reaches the highest when all auxiliary datasets are added,with OA=92.34%and Kappa coefficient=0.91.The classification validity is verified using the categorical re-gression tree algorithm,it shows that the classification effect based on the random forest algorithm is better than that of the categorical regression tree algorithm.The SNIC segmentation algorithm based on the remote sensing cloud platform is integrated into an auxiliary data set for object-oriented classifica-tion,which provide a reference for improving the accuracy of land use classification.
land use classificationauxiliary datasetsSNIC segmentationobject-orientedrandom forestsentinel-2A image
国家科技期刊平台
NSTL
万方数据
维普
