COMBINED INTERPOLATION RESTORATION OF LANDSAT IMAGES THROUGH FIR FILTER DESIGN TECHNIQUES

In digital image processing for remote sensing there is often a need t o interpolate an image. Examples occur in scale magnification, image r egistration, geometric correction, etc. On the other hand, this image can be subject to several sources of degradation and it would be inter esting to compensate also for this degradation in the interpolation pr ocess. Therefore, this article addresses the problem of combining inte rpolation and restoration in a single operation, thereby reducing the computational effort. This is done by means of two-dimensional, separa ble, Finite Impulse Response (FIR) filters. The ideal low pass FIR fil ter for interpolation is modified to account for the restoration proce ss. The Modified Inverse Filter (MIF) and the Wiener Filter (WF) are u sed for this purpose. The proposed methods are applied to the interpol ation-restoration of Landsat-5 Thematic Mapper data. The later process takes into account the degradation due to optics, detector and electr onic filtering. A comparison with the Parametric Cubic Convolution (PC C) technique is made. The experimental results consist of interpolatio n-restoration processes of Landsat-5 Thematic Mapper images from 30 m to 15 m (scale magnification) but they could also be generalized to in clude deblurring on more general interpolation problems, like geometri c correction.