Classification of multisensor remote sensing data based image fusion

Decision fusion can be defined as the process of fusing information from individual data sources after each data source has undergone a preliminary classification. In this paper, a combination of multi-level neural networks decision fusion schemes will be tested in classification of multisource and hyperdimensional data sets. The integrated features of the multispectral image to classify image's texture is used, namely, the two types parameters are estimated as the texture features: the Hurst parameter and the unit displacement incremental power. The efficiency of the features is evaluated by comparing several other features with them. The performance of the above approaches with the use of different feature was investigated. The algorithm presented in the paper was found to be more efficient than other spatial methods.