Image Processing

Pre-image Processing: Prior to data analysis, initial processing on the raw data is usually carried out to correct for any distortion due to the characteristics of the imaging system and imaging conditions. Its depend on the user's. These procedures include radiometric correction and field of view of the sensor.

Image Enhancement: It used to visual interpretation and understanding of imagery. Attempted after image is corrected for distortions. Linear contrast enhancement, also referred to as a contrast stretching, linearly expands the original digital values of the remotely sensed data into a new distribution. By expanding the original input values of the image, the total range of sensitivity of the display device can be utilized.

Type of enhancement

RADIOMETRIC ENHANCEMENT  - Modification of brightness values of each pixel in an image data set independently (Point operations).

SPATIAL ENHANCEMENT - Modification of pixel values based on the values of surrounding pixels. (Local operations)

SPECTRAL ENHANCEMENT- Enhancing images by transforming the values of each pixel on a multiband basis

Linear contrast enhancement: Linearly expands the original digital values of the remotely sensed data into a new distribution. These types of enhancements are best applied to remotely sensed images with all the brightness values. A DN is assigned extreme black(0) extreme white(255). There are three methods of linear contrast enhancement:

               Minimum-Maximum Linear Contrast Stretch

               Percentage Linear Contrast Stretch

               Piecewise Linear Contrast Stretch

Linear contrast stretch

 Input and Output Data Values follow a linear relationship

             • Min – Max Stretch

             • % stretch

             • Std. deviation Stretch

             • Piecewise Linear Stretch

             • Saw tooth Stretch

Non Linear contrast enhancement; Input and output data values do not follow a linear relationship. It included Square, Exponential, Logarithmic, log. Input and Output Data Values do not follow a linear relationship

            • Logarithmic

            • Inverse Log

            • Exponential

            • Square

            • Square root 

What is Spatial Filtering;

It is a process of dividing the image into its constituent spatial frequencies to emphasize some image features. This technique increases the analyst ability to discriminate detail. Main purpose of this technique is to: enhance certain features, remove the noise in the image, smoothness of image.

Low pass filter:

      It eliminate high frequency components, while leaving low frequencies untouched. It enhance the block high frequency, Smoothening effect on images, Removal of noise, Blurring of image especially at edges.

Low pass filter type,

        Mean – Smoothen the image and blurring the edge

        Median- Retain sharpness and smoothening of image

        Mode – Replace by most common neighbor in the window

High pass filter;

       Removes slowly varying components and preserves high frequencies, Emphasizes fine details, Use for edge detection and enhancement. Edges and locations where transition from one category to other occurs. Edge detection and enhancement. 

NDV I- Normalized different vegetation index-

       Monitor vegetation condition on continental and global scales using the advanced very high resolution Radiometric. The range of -1.0 to 1.0 increasing positive NDVI values. It calculated as

NDVI=B4-B3/B4+B3

Types of scattering are ;

      1. Rayleigh scattering

      2. Mie scattering

      3. Nonselective scattering Rayleigh

scattering : occurs when particles are very small compared to the wavelength of the radiation. These could be particles such as small specks of dust or nitrogen and oxygen molecules. 

Mie scattering : occurs when the particles are just about the same size as the wavelength of the

radiation. Dust, pollen, smoke and water  vapors  are common causes of Mie scattering which

tends to affect longer wavelengths than those affected by  Rayleigh scattering.

Radiometric Correction- It included correcting the data for unwanted sensor or atmospheric noise and converting the data.

Geometric 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.

Why Geometric correction;

•To allow an image to overlay a map.

•To warp an image to eliminate distortion. caused by terrain, instrument wobble, earth curvature, etc.

•To change the spatial resolution of an image.

•To change the map projection.

• Sources of distortions are-

• Variation in the altitude

• Altitude & Velocity of the sensor platform

• Earth curvature• Atmospheric refraction

• Relief displacement and

• Nonlinearities in the sweep of a sensor’s IFOV

Georeferencing; Conversion of the data to real world coordinates are carried by analyzing well distributed Ground Control Points (GCPs). This is done in two steps Georeferencing and Geocoding

Georeferencing : This involves the calculation of the appropriate transformation from image to terrain coordinates.

Geocoding :This step involves resembling the image to obtain a new image in which all pixels are correctly positioned within the terrain coordinate system. Georeferencing with additional resampling the image so that the pixels are exactly positioned within the map system. When Geocording images it involves resembling the image to obtain a new image in which all pixels are correctly positioned within the terrain coordinate system.

Coordinate system: File coordinate- Refer to the location of the pixels within the image file. This pixel value starting in upper left corner of the image 0,0

Map coordinates : Expressed in one of a number of map coordinate or projection system.

Color Composit; Singalband images are normally display from black(0) to white (255). Colour images display combination of 3 bands, red, green and blue.

R<G<B – colour

R      255       0       255

G          0    255         0

B      255       0       255

          R        G         B

In general three techniques are used :

– Natural color composite

– Pseudo natural color composite

– False color composite

Natural Color Composite

• For a Natural Color Composite one should use the spectral bands Blue, Green and Red and display them in Blue, Green and Red on a graphical screen.

R     G     B

3      2      1

R      G    B

• Actually to get a true natural scene the spectral signature of the screen should be equal to the pectral signature of the satellite.

Red Green Blue

Red Green Blue

Pseudo natural color composite For a Pseudol Color Composite one should use the spectral bands Red,

NIR and Green and display them in Red, Green and Blue on a graphical screen.

R     NIR     G

3      4/5      2

R      G       B

False color composite  For a False Color Composite one should use the spectral bands NIR, Red and Green and display them in Red, Green and Blue on a graphical screen.

NIR   R      G

4       3       2

R      G       B

Band 1  -Blue

Band 2  -Green

Band 3 – Red

Band4- Near IR

Band 5 – Near IR

Band 6 – Thermai IR

Band 7 – Mid IR

NCC – 3,2,1

FCC – 4,3,2

PNCC – 3,4,2