Spatial Enhancement by ERDAS

Aim: - To perform Spatial Enhancement by using Convolution and Crisp Techniques

Procedure:-

Start ERDAS Imagine by click Start/ Programs/ ERDAS IMAGINE / ERDAS

IMAGINE

To start Radiometric Enhancement click on the Interpreter icon on the ERDAS

IMAGINE icon panel.

This will displays the Image Interpreter DBX

Click on the Spatial Enhancement Tab, this will displays the Spatial

Enhancement DBX

1. Convolution: Convolution filtering is the process of averaging small sets of pixels

across an image. Convolution filtering is used to change the spatial frequency

characteristics of an image

Convolution Formula:

The following formula is used to derive an output data file value for the pixel

being convolved (in the center):

Where:

fij = the coefficient of a convolution kernel at position i,j (in the kernel)

dij = the data value of the pixel that corresponds to fij

q = the dimension of the kernel, assuming a square kernel (if q = 3, the kernel

is 3 × 3)

F = either the sum of the coefficients of the kernel, or 1 if the sum of

coefficients is 0

V = the output pixel value

In cases where V is less than 0, V is clipped to 0.

The sum of the coefficients (F) is used as the denominator of the equation

above, so that the output values are in relatively the same range as the input

values. Since F cannot equal zero (division by zero is not defined), F is set to 1

if the sum is zero.

Click on the Convolution tab from the Spatial Enhancement DBX, this will

display Convolution DBX

Browse for the input file and Give the name of Output file by browsing the

destination folder

Select the default kernel in the kernel library

Select 3*3 Edge Detect from kernel List

Select coordinate type map

Select the radio button against reflection under Handle Edge By tab

Put tick against the Check box of Ignore Zero Stats

Put tick against the check box of Normalize the Kernel

Click OK, to perform Convolutionclick OK in the Modeler DBX after the

completion of job.

Result: -

2. Crisp:

The Crisp filter sharpens the overall scene luminance without

distorting the interband variance content of the image. This is a useful

enhancement if the image is blurred due to atmospheric haze, rapid sensor

motion, or a broad point spread function of the sensor.

The algorithm used for this function is:

1) Calculate principal components of multiband input image.

2) Convolve PC-1 with summary filter.

3) Retransform to RGB space.

The logic of the algorithm is that the first principal component (PC-1)

of an image is assumed to contain the overall scene luminance. The other

PCs represent intra-scene variance. Thus, you can sharpen only PC-1 and

then reverse the principal components calculation to reconstruct the

original image. Luminance is sharpened, but variance is retained.

Crisp gray Scale Model:

Click on the Crisp tab from the Spatial Enhancement DBX, this will display

Crisp DBX

Browse for the input file and Give the name of Output file by browsing the

destination folder

Select coordinate type map

Put tick against the Check box of Stretch to Unsigned 8 bit

Put tick against the check box of Ignore Zero Stats

Click OK, to perform Crispclick OK in the Modeler DBX after the

completion of job.

Result: -