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There are two data sources; analog data and digital data. Digital data, for example: - multi-spectral scanner data is converted from HDDT (high-density digital tape) to CCT (computer compatible tape) for ease use of computer analysis. Analog data for example: - an image scanner or drum scanner into a digital image data form must digitize aerial photographs. Image rectification and restoration These operations aim to correct distorted or degraded image data to create a more faithful representation of the original scene. This typically involves the initial processing of raw image data to correct for geometric distortions, to calibrate the data radiometrically, and to eliminate noise present in the data (Lillesand and Kiefer, 1994). Thus, the nature of any particular image restoration process is highly dependent upon the characteristics of the sensor used to acquire the image data. Image rectification and restoration procedures are often termed preprocessing operations because they normally precede further manipulation and analysis of the image data to extract specific information. As with geometric correction, the type of radiometric correction applied to any given digital image data set varies widely among sensors. Other things being equal, the radiance measured by any given system over a given object is influenced by such factors as changes in scene illumination, atmospheric conditions, viewing geometry, and instrument response characteristics. Some of these effects, such as viewing geometry variations, are greater in the case of airborne data collection than in satellites image acquisition. Also, the need to perform correction for any or all these influence depends directly upon the particular application at hand (Lillesand and Kiefer, 1994). Image noise is any unwanted disturbance in image data that is due to limitations in the sensing, signal digitization, or data recording process. The potential source of noise range from periodic drift or malfunction of a detector, to electronic interference between sensor components, to intermittent “hiccups” in the data transmission and recording sequence. Noise can either degrade or totally mask the true radiometric information content of a digital image. Hence, noise removal usually precedes any subsequent enhancement or classification of the image data. The objective is to restore an image to as close an approximation of the original scene as possible (Lillesand and Kiefer, 1994). Raw digital images usually contain geometric distortions so significant that they cannot be used as maps (Lillesand and Kiefer, 1994). This procedure is undertaken to avoid geometric distortion from a distorted image, and is achieved by establishing the relationship between the image coordinate system and the geometric information system using calibration data of the sensor, measured data of position and altitude, ground control points, atmospheric condition etc. The sources of these distortions range from variation in altitude, attitude, and velocity of the sensor platform, to factors such as panoramic distortion, earth curvature, atmospheric refraction, relief displacement, and non-linearity’s in the sweep of a sensor (Lillesand and Kiefer, 1994). The intent of geometric correction is to compensate for distortions introduced by these factors so that the corrected image will have the geometric integrity of a map. Additional problems include the inability to directly relate the image to other spatially referenced data and the fact that images taken of the same area at different time will not be registered. Geometric corrections are required. For this study, the procedure and technical term used for correct the geometric distortions are: |
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