Unsupervised classification of high spatial remote sensing image combining L0 smoothing and superpixel

Aiming at the problem that the unsupervised classification method is easy to form “salt and pepper” noise and generate many errors and missed points in the classification of high spatial remote sensing images, an unsupervised classification method of high spatial remote sensing image based on L0 smoothing and superpixel is proposed. Firstly, a L0 algorithm is instituted to smooth the high space remote sensing image and reduce a range of image noises and redundant information. Then, a superpixel method of SLIC (Simple Linear Iterative Clustering) is used for that further inhibiting the salt and pepper phenomenon while reducing the processing complexity, and the initial clustering map is obtained. Finally, the K-means unsupervised classification method is established to receive the final classification result image. Furthermore, three high spatial remote sensing images are selected as experimental data to verify the method proposed in this paper. The experimental results show that the proposed method can achieve accurate classification of features, and the overall accuracy is 72.46％, 77.55％ and 78.44％ respectively. The Kappa coefficients are 0.788, 0.779 and 0.779 respectively. The proposed method can effectively solve the phenomenon of “salt and pepper” in the classification,  improve the classification accuracy and have certain reference value for high spatial remote sensing image classification.