Unsupervised land-cover classification through hyper-heuristic-based Harmony Search

Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of optimizing the well-known k-means algorithm by combining different variations of the Harmony Search technique using Genetic Programming (GP). We have shown GP can improve the recognition rates when using one optimization technique only, but it still deserves a deeper study when we have a very good individual technique to be combined.