부산대학교 도서관

The impairments produced in image sensors and communication channels degrade image quality and introduce significant errors in the results of data fusion. To improve image fusion results in the presence of different types of impairments we propose a two-stage approach. New multistage nonlinear locally-adaptive image processing algorithms are developed and applied at the first stage to mitigate image impairments such as geometric distortions due to communication system synchronization errors, narrowband frequency interferences and sensor noise. Image fusion and classification algorithms based on artificial neural networks and support vector machines are used at the second stage. Experimental results are presented for real satellite remote sensing images and simulated data providing quantitative assessment of the proposed algorithms.