digital image processing definition: computer-based manipulation and interpretation of digital...
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Digital Image Processing
• Definition: Computer-based manipulation and interpretation of digital images.
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Digital Image Processing
• Five broad types of computer assisted operation:
1. Image rectification and restoration (preprocessing)2. Image enhancement3. Image classification (spectral vs. spatial pattern
recognition)4. Data merging and GIS integration5. Hyperspectral image analysis6. Biophysical modeling7. Image transmission and compression
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Image Rectification & Restoration
• Preprocessing to correct distorted or degraded image data
– Geometric distortions– Radiometric calibration– Elimination of noise
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Image Enhancement
• To more effectively display or record the data, increasing the visual distinctions between features in a scene
• Contrast manipulation: stretching
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Image Enhancement
• Spatial feature manipulation: – Filtering– Convolution– Edge enhancement– Fourier
• Enhancement involving multiple bands of an image
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Image Classification
• To determine the land cover identity of each pixel in an image, replacing visual analysis with quantitative techniques
• Spectral pattern recognition:– using only spectral radiances
• Spatial pattern recognition: – using geometric shapes, sizes and patterns
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Image Classification
• The overall objective of classification is to categorize all pixels in a digital image into one of several land cover classes
• Themes thematic maps: A map designed to demonstrate particular features or concepts.
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Examples of Classification Results
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Data Merging and GIS Integration
• To combine image data with other geographically referenced datasets for the same area
– Multitemporal data merging– Change detection procedures– Multisensor image merging
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Biophysical Modeling
• To relate quantitatively digital data (recorded by a RS system) to biophysical features and phenomena measured on the ground
• Allows us to simulate the functioning of environmental systems in a spatially explicit manner and to predict their behavior under “what if” conditions
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Image Rectification & Restoration
• To correct image data for distortions or degradations stemming from the image acquisition process
• Varies with: – Type of device (camera, scanner)– Platform (airborne versus satellite)– Total field of view- A sensor with a wide field of view can
image the same place from different angles more frequently than a sensor in the same orbit with a narrower field of view and smaller swath
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Image Rectification & Restoration
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Digital images
• Remote sensing images are recorded in digital forms
• A digital image is a two-dimensional array of pixels.
• Each pixel has an intensity value (represented by a digital number) and a location address (referenced by its row and column numbers).
• They are distributed in computer compatible tapes (CCTs)
• The basic unit is pixel that is represented as a digital number (DN)
• Different data sets may use different number of bits so that bit scaling is common.
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Digital parameters for Landsat and SPOT images
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Bit scales for Landsat and SPOT sensors
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Image processing systems
• Computer hardware (e.g. PC) • Computer software • Operating system (e.g. Windows/NT) • Image processing software (e.g. ENVI) • Operator (e.g. you)
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Geometric Correction• Raw digital images usually contain geometric
distortions so significant that they cannot be used directly as a map base without subsequent processing.
• Distortion-changes in shape and position of objects with respect to their true shape and position.
• The source of distortions:– Variations in altitude, attitude, and velocity of sensor
platform– Panoramic distortion– Earth curvature– Atmospheric refraction– Relief displacement
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Geometric Correction
• The geometric correction process is normally implemented as a two-step procedure:– Systematic (predictable) v. random
(unpredictable) distortions
– Systematic: corrected through modeling distortion sources
• e.g. eastward rotation of the earth (skew distortion) deskewing
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Geometric Correction
• Random: corrected by analyzing well-distributed ground control points
• Two-coordinate transformation equationsx=f1(X,Y) y=f2(X,Y)
x,y: distorted image coordinates (column, row) X,Y: correct (map) coordinates f1,f2= transformation function• Resampling (image registration)
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Image registration (or resampling)
• Resampling-The calculation of new DN for pixels created during geometric correction of a digital scene, based on the values in the local area around the uncorrected pixels.
• In the Nearest Neighbor technique, the transformed pixel takes the value of the closest pixel in the pre-shifted array.
• Bilinear Interpolation:uses the 4 closest pixel values surrounding the transformed output pixel is used.
• Cubic Convolution: uses the 16 closest pixel values
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Image registration (or resampling)
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Image registration (or resampling)