Digital Image Processing

• Definition: Computer-based manipulation and interpretation of digital images.

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

Image Rectification & Restoration

• Preprocessing to correct distorted or degraded image data

– Geometric distortions– Radiometric calibration– Elimination of noise

Image Enhancement

• To more effectively display or record the data, increasing the visual distinctions between features in a scene

• Contrast manipulation: stretching

Image Enhancement

• Spatial feature manipulation: – Filtering– Convolution– Edge enhancement– Fourier

• Enhancement involving multiple bands of an image

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

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.

Examples of Classification Results

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

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

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

Image Rectification & Restoration

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.

Digital parameters for Landsat and SPOT images

Bit scales for Landsat and SPOT sensors

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)

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

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

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)

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

Image registration (or resampling)

Image registration (or resampling)