Nonlinear separation source and parameterized feature fusion for satelite image patch exemplars

We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation in the feature extraction stage. Feature information presentation is preliminary in machine learning or machine vision projects and provides an efficient and reliable data presentation than original data. Fusing feature aims to enrich the information characteristics about the land cover namely textural information, contours and multi-resolution information. Parameterized fusion model aim to determine the best feature weights in terms of data classification. Finally, a machine learning classification method is used for remote sensing data base. Experimental results show that the proposed fusion method enhances the classification accuracy and provide powerful tool for image exemplars classification.