Land Cover Classification Method of Remote Sensing Image Based on Optimal Scale
Remote sensing images of land cover is a strategic basic resource of the country.Real,accurate and re-al-time information of land-cover type is crucial to scientific protection and rational utilization of land resources.In the era of big data,the number of remote-sensing images is growing rapidly,and the accuracy and stability of existing algorithms cannot meet the needs of land-cover classification.This paper proposes a method based on the combination of optimal scale segmentation and feature fusion.The method comprises the following steps.Firstly,an optimal scale for segmentation is calculated according to the local variance of a remote sensing image,and the over-segmented and under-segmented images are optimized by taking the scale as a reference.Secondly,the texture feature and the prima-ry feature of a land image are extracted by taking the segmented image as a reference with the local binary pattern(LBP)operator and neural network.Finally,after image preprocessing,the two features of the image are fused to be the input of a support vector machine(SVM)classifier as a base kernel.The classification model of land remote sensing image based on optimal scale segmentation and feature fusion(OSF-SVM model)is constructed.The simula-tion results of segmentation experiments show that,compared with the existing methods,the scale segmentation tech-nology in this paper has improved RR,RI and ARI indicators,and the average score has increased by 10.83%.The simulation results of classification experiments show that,compared with the traditional SVM model,the OSF-SVM model has an average increase of 0.041,0.039 and 0.040 in R,P and F1 indicators,respectively.Therefore,the re-mote sensing image land-cover classification model constructed in this paper improves the accuracy and stability of image segmentation and classification through optimal scale segmentation and feature fusion.
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