Remote sensing change detection based on Growing Hierarchical Self-Organization Map

Remote sensing change detection is a hot issue in recent years. However, most methods originate from statistical pattern recognition. Parameter resolution and time-consuming are main disadvantages of these methods. Hence, in this paper we propose a novel remote sensing change detection method which originates from neural network pattern recognition. The method is based on Growing Hierarchical Self Organization Map (GHSOM). GHSOM has flexible network architecture to adjust remote sensing scene complexity. Theoretically speaking, GHSOM is able to extract change areas well. In experiment, we select three pairs of remote sensing image. We compare the results with Gaussian Mixture Model result and traditional SOFM result. The experiment shows the proposed method is advantageous in efficiency and detection accuracy. It can be expected that the method will be applied in GIS data updating, land use cover surveying, and natural disaster evaluation.