Object-oriented Land Cover Classification In High Resolution SAR Images

Faced with massive high-resolution SAR image data,it is unsatisfied with conventional pixel-based method because of its difficulty in characterizing actual geographic objects and the lack of spatial information.The academic community has proposed an Object-based Image Analysis paradigm to solve the problem of remote sensing image interpretation.In addition,superpixel-based methods are also widely used to simplify image processing.By grasping commonality between superpixels and objects,superpixels can be introduced into the Object-based Image Analysis paradigm.The object-oriented algorithm generally applied to optical images is not ideal for segmentation of SAR images with speckle noise.Therefore,the research on Object-based Image Analysis paradigm for SAR images is one of the current research hotspots.This paper is developed on the subject of object-oriented land cover classification in high resolution SAR Images.Firstly,a novel SAR image superpixel generation method based on edge intensity feature and linear spectral clustering for SAR images is proposed.Combined with other superpixel segmentation algorithms,the multiscale image segmentation of SAR image is realized by pre-extracting superpixel segmentation sets of different scales.Then some typical feature extraction algorithms are analyzed in next part,and the SAR image feature analysis method based on superpixel is discussed.Finally,on the basis of a set and set distance classification algorithm,the algorithm of superpixel subset generation is improved,and finally the SAR image land cover classification based on superpixel is realized.In summary,this paper mainly completes the land cover classification of SAR image based on superpixel-based object-oriented paradigm,and verifies the feasibility of the method through related experiments.