In defense of iterated conditional mode for hyperspectral image classification

Hyperspectral image classification is one of the most significant topics in remote sensing. A large number of methods have been proposed to improve the classification accuracy. However, the improvement often comes at the cost of higher complexity. In this work, we mainly focus on the Markov Random Fields related paradigm, which involves a demanding energy minimization procedure. Traditional methods are prone to employ the advanced optimization techniques. On the contrary, this paper is in defense of a simple yet efficient method for hyperspectral image classification, Iterated Conditional Mode, which has been generally considered inferior to other state-of-the-art methods. Our purpose is successfully achieved by tackling two inherent drawbacks of ICM, sensitive label initialization and local minimum. We apply our method to three real-world hyperspectral images, and compare the results with those of state-of-the-art methods. The comparisons show that the proposed method outperforms its competitors.