GEOG 330 Image Processing 1

GEOG 330 Image Processing 1 - Digital Image Processing...

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Digital Image Processing – Preprocessing and Enhancement
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Ideally, the radiance recorded by a remote sensing system in various bands is an accurate representation of the radiance actually leaving the feature of interest (e.g., soil, vegetation, water, or urban land cover) on the Earth’s surface. Unfortunately, noise (error) can enter the data-collection system at several points. For example, radiometric error in remotely sensed data may be introduced by the sensor system itself when the individual detectors do not function properly or are improperly calibrated. Several of the more common remote sensing system–induced radiometric errors are: • random bad pixels (shot noise), • line-start/stop problems, • line or column drop-outs, • partial line or column drop-outs, and • line or column striping. Image Preprocessing – Noise Removal Jensen, 2004
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Image Preprocessing – Noise Removal Jensen, 2004
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Image Preprocessing – Radiometric Calibration Jensen, 2004 Digital Number to Radiance Calibration Based Upon Pre- and/or Post-Launch Sensor Calibration Equation of a Line: Y = mX + b m = slope b = intercept Radiance = m(DN) +/- b m = sensor gain b = sensor offset
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Image Preprocessing – Atmospheric Correction Jensen, 2004 There are several ways to atmospherically correct remotely sensed data. Some are relatively straightforward while others are complex, being founded on physical principles and requiring a significant amount of information to function properly. This discussion will focus on two major types of atmospheric correction: Absolute atmospheric correction , and Relative atmospheric correction . There are various methods that can be used to achieve absolute or relative atmospheric correction. The following sections identify the logic, algorithms, and problems associated with each methodology.
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Image Preprocessing – Atmospheric Correction Radiative Transfer Modeling Jensen, 2004
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