Combined interpolation—restoration of Landsat images through FIR filter design techniques

Abstract

Abstract. In digital image processing for remote sensing there is often a need to interpolate an image. Examples occur in scale magnification, image registration, geometric correction, etc. On the other hand, this image can be subject to several sources of degradation and it would be interesting to compensate also for this degradation in the interpolation process. Therefore, this article addresses the problem of combining interpolation and restoration in a single operation, thereby reducing the computational effort. This is done by means of two-dimensional, separable, Finite Impulse Response (FIR) filters. The ideal low pass FIR filter for interpolation is modified to account for the restoration process. The Modified Inverse Filter (MIF) and the Wiener Filter (WF) are used for this purpose. The proposed methods are applied to the interpolation-restoration of Landsat-5 Thematic Mapper data. The later process takes into account the degradation due to optics, detector and electronic filtering. A comparison with the Parametric Cubic Convolution (PCC) technique is made. The experimental results consist of interpolation-restoration processes of Landsat-5 Thematic Mapper images from 30 m to 15 m (scale magnification) but they could also be generalized to include deblurring on more general interpolation problems, like geometric correction

Image Fusion for Remote Sensing Applications

“…www.intechopen.com As the spatial resolution difference between CCD and HRC is large, firstly, we resample the CCD images to 10 meter spatial resolution by applying the restoration procedure (Fonseca et al, 1993). The restoration filter takes into account the spatial response of each sensor to resample and restore the image in a single processing step.…”

“…Table 2 presents the characteristics of HRC, CCD and QB sensors. The CBERS-2B images are pre-processed using restoration (Fonseca et al, 1993), noise filtering and orthorectification procedures. Afterwards, the images are fused and classified for mapping the remaining forest in the Ibitinga Resevoir.…”

Analysis of the Relationship Between Intra-Urban Vegetation Change and Socio-Economic Data

“…Next, we performed the atmospheric correction through the subtraction technique of the dark pixel (Chavez, 1988). Subsequently, we processed the images with a restoration filter, which improves the effective spatial resolution of the image and interpolates them at a finer sampling grid (Fonseca et al, 1993). The pixel size of CCD (20 m) and TM (30 m) was changed to 10 meters using the aforementioned restoration algorithm.…”

Convex restriction sets for CBERS‐2 satellite image restoration

“…The Modified Inverse Filter, also called Transfer Function Compensation, approximates the Inverse Filter and at the same time attempts to control the problems associated with it. The idea is to choose a desired function D as the response of the system that would alleviate the ill-conditioning effects (Fonseca et al 1993): …”

“…Some works oriented towards remote sensing can be cited: Fonseca et al (1993) developed the Modified Inverse Filter, which is a regularized version of the Inverse Filter to restore and interpolate Landsat images; Wu and Schowengerdt (1993) dealt with the restoration of images containing mixed pixels; Bhaskar et al (1994) tackled the problem of lens defocus and linear motion blur in Space Shuttle images; Reichenbach et al (1995) described the design of small convolution kernels for the restoration and reconstruction of Advanced Very High Resolution Radiometer (AVHRR) images; Boo and Bose (1997) applied image restoration techniques to multispectral images; Jalobeanu et al (1993) used complex wavelet packets to deconvolve degraded images; Likhterov and Kopeika (2002) dealt with the problem of vibration in images with a differential scheme; Chen and Xanju (1994) The POCS method uses a priori knowledge about the image or the imaging system. The key to effectively apply this kind of algorithm is to define the appropriate sets, compute the projection onto these sets, and incorporate the projectors into an image processing algorithm designed to meet some criteria implied by the constraints (Stark 1998).…”