Landmark-Isometric mapping (L-ISOMAP),as a dimensionality reduction method,has great potential in hyperspectral imagery visualization.There are two problems in L-ISOMAP algorithm,i.e.,the computational cost is high and the landmarks lack of representation for hyperspectral imagery.In this case,an improved L-ISOMAP algorithm,named KL-ISOMAP,is proposed based on K-medoids clustering algorithm.The KL-ISOMAP algorithm consists of the following steps:1)Selecting the landmarks by the improved K-medoids algorithm;2) Removing the similar pixels according to the similarity;3) Implementing the non-linear dimensionality reduction of the rest pixels;4) Implementing visualization on the reduced dataset.Experimental results show that KL-ISOMAP algorithm can improve the intrinsic structure representation of the landmarks and therefore improve the visualization performance.Furthermore,the algorithm can be speeded up by setting the similarity threshold.The visualization method is reasonable,feasible and of good visual effect,and has good performance in terms of feature distance and class separability preserving for hyperspectral imagery.