Fusion classification of hyperspectral image based on adaptive subspace decomposition

Data fusion is widely used in many fields in the last two decades. With the development of hyperspectral sensor technique, the concept of data fusion is introduced into the classification investigation of hyperspectral data recently. Consensus theory is one of the data fusion methods, in which how to properly choose and assign the weights is very important for the improvement of fusion classification accuracy. In this paper, a new method of hyperspectral image classification is studied, which is realized by two key steps: division of data sources based on adaptive subspace decomposition (ASD) and fusion classification based on consensus theory. In order to testify the effectiveness of the proposed method, computer simulations are conducted on AVIRIS data. The experiment investigation shows that the classification result in our new method is improved compared with both the equal weights and conventional approach in the full data space.