5A

• Manipulation and interpretation of digital image with the aid of computer

  Digital Image Interpretation and Analysis
• Enhance image as a prelude for visual interpretation.

  Digital Image Processing
• Identify target and extract information without manual intervention by a human interpreter.

  Digital Image Processing
• Central Idea of Digital Image Processing?

  Digital image is feed into computer one pixel at a time, using image processing software or program.

  Using the software or program the data is inserted into an equation or series of equations and store the results of computations for each pixel.

  Result form a new digital image data that can be recorded in pictorial format.
• Brightness or color of image

  Tone
• Fundamental element to distinguish between different target or features.


  Toone
• Shape, Texture, and Pattern to be distinguished.

  Variations in Tone
• General form, structure, or outline of individual object.


  Shape
• Function of scale

  Size
• Size of a target - scene

  Absolute size - interpretation of target
• Spatial Arrangement of visibly discernible objects.

  Pattern
• Orderly repetition of similar texture and tones.

  Pattern
• Arrangement and frequency of tonal variation

  Most important element imagery

  Texture
• Mottled tones, grey levels; small area.

  Rough Texture
• Very little tonal variation; uniform and even surface.

  Smooth texture
• Idea of profile and relative height

  Shadow
• Reduces or eliminate interpretation in area of influence.

  Shadow
• Topography and land forms... radar imagery ; less

  Shadow
• Relationship between other recognizable objects or features in the proximity to the of interest

  Association
• Associate with other features.

  Association
• Digital Image Processing procedures

  Formatting and collecting of data.

  Digital Enhancement to facilitate better visual interpretation.

  Automated classification of targets and features entirely by computers
• Common Image Processing Function

  Preprocessing

  Image Enhancement

  Image Transformation

  Image Classification and Analysis
• Correct for sensor- and platform-specific radiometric and geometric distortion of data.


  Image Preprocessing
• Normally required prior to the main data; Radiometric and Geometric Calibration.

  Preprocessing
• Correcting the data for sensor irregularities and unwanted sensor or atmospheric noise and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor.


  Radiometric Correction
• Three groups of radiometric corrections are identified:

  cosmetic’ rectification to compensate for data errors.

  relative atmospheric correction based on ground reflectance properties.

  absolute atmospheric correction based on atmospheric process information.
• Corrections are typically executed (if required) at the satellite data receiving stations or image pre-processing centers, before reaching the final user.


  Cosmetic correction
• Typical problems requiring cosmetic corrections are

  Periodic line dropout. (recording prob/wrong data)

  Line stripping. (non-identical detector/ drift H/L)

  Random noise/spike corrections (digital filt/transmission of data/temporary disturbance
• Occur due to recording problems when one of the detectors of the sensor in question either gives wrong data or stops functioning

  Periodic line dropouts
• is far more common than line dropouts.

  Line striping
• often occurs due to non-identical detector response. With time the response of some detectors may drift to higher or lower level


  Line striping
• requires a more sophisticated restoration method such as digital filtering.


  Random noise
• errors during transmission of data or to a temporary disturbance.

  Random noise or spike noise
• individual pixels acquire DN-values that are much higher or lower than the surrounding pixels.

  Random noise or spike correction
• include correcting for geometric distortions due to sensor-Earth geometry variations, and conversion of the data to real world coordinates on the Earth's surface.

  Geometric corrections
• Several factors for geometric distortions, including:

  perspective of the sensor optics motion of the scanning system

  motion of the platform

  platform altitude, attitude, and velocity

  terrain relief

  curvature and rotation of the Earth.
• No remote sensing image is free of geometric distortions and an essential requirement for integrated processing of RS images and data from GIS is that they are spatially referenced.
• e geometric distortions inherent in remote sensing images fall into two categories: ✓ systematic distortions ✓ nonsystematic distortions
• Many factors contribute to the systematic distortions.

  Panoramic distortion

  Platform velocity

  Curvature of the earth

  Earth rotation

  Scan skew

  Mirror scan velocity
• Systematic Distortion - Distortions of this type can be rectified using data from platform ephemeris* and knowledge of internal sensor distortion *any tabular statement of the assigned places of a celestial body for regular intervals.