Classification of high spatial resolution remote sensing imagery based on object-oriented multi-scale weighted sparse representation

In this paper, according to the multi-scale advantage for high spatial resolution remote sensing imagery and the influence difference among multi-scale objects for classification, the objected-oriented multi-scale weighted sparse representation classification algorithm is proposed by taking the advantages of object-based image analysis method and sparse representation classification algorithm. Firstly, the multi-scale segmentation results are obtained and the multi-scale features are extracted by the multi-scale segmentation algorithm; secondly, the object weights in each scale are computed according to multi-scale segmentation quality measure, and the objected-oriented multi-scale weighted sparse representation model is constructed; finally, the two domestic GF-2 high spatial resolution remote sensing images and one high-spatial and spectral resolution dataset (Washington D.C. data) were adopted to verify the proposed algorithm. The experiment results show that the proposed algorithm can obtain the highest classification accuracy with OA and Kappa, efficiently improve classification accuracy at each scale objects, reduce salt and pepper noise in the classification results, and respectively maintain the regional integrity in the large scale objects and the details in the small scale objects comparing with the traditional SVM, pixel sparse representation, single scale and multi-scale sparse representation and object-based deep learning methods. © 2022, Surveying and Mapping Press. All right reserved.