Image Enhancement Part-II (Spatial, Spectral and Radiometric Enhancement)
There are several others methods to improve quality of image for better interpretation. These enhancement methods are divided on the basis of three basic image concepts of spatial resolution, spectral resolution and radiometric resolution. Therefore they called as spatial enhancement, spectral enhancement and radiometric enhancement.
Spatial Enhancement
As it called spatial enhancement so it enhances spatial resolution properties of a satellite image. Spatial resolution defined as ability of sensor to distinguish the small objects. It refers to the amount of detail that can be detected by a sensor. Image pixel size defines the spatial resolution of a satellite image.
For detailed mapping of land use practices requires a much greater spatial resolution. If enhance the spatial properties of image to get more detailed and accurate information one should use this spatial enhancement tool.
This enhancement tool has two important features such as convolution and resolution merge. Resolution merge is one of the best tools to improve the spatial resolution of a satellite image. such as LISS-4 image (Color satellite image) of India satellite has 5.8m spatial resolution when it merge with Cartosat image (Black and White satellite image) of 2m/1m then the outcome image become color satellite image of 2m/1m. So, one have achieved a color satellite image with higher resolution.
In the below explanation an example of convolution enhancement method is discussed.
Apply Spatial Enhancement to an image 1. Click the Interpreter icon on the ERDAS IMAGINE icon panel.
The Image Interpreter menu will open as above in image
1. Select Spatial Enhancement from the Image Interpreter menu and the Spatial Enhancement menu opens.
2. Select Convolution from the Spatial Enhancement menu and the Convolution dialog opens.
This interactive Convolution tool lets you perform convolution filtering on images. It provides a scrolling list of standard filters and lets you create new kernels. The new kernels can be saved to a library and used again at a later time.
Select Input/Output Files
1. In the Convolution dialog, under Input File, enter jaipur.img.
2. Under Output File, enter convolve.img in the directory of your choice. It is not necessary to add the .img extension when typing the file name ERDAS IMAGINE automatically appends the correct extension.
NOTE: Make sure you remember in which directory the output file is saved. This is important when you try to display the output file in a Viewer.
Select Kernel
1. You must select the kernel to use for the convolution. A default kernel library containing some of the most common convolution filters is supplied with ERDAS IMAGINE. This library opens in the Kernel Selection part of this dialog.
2. From the scrolling list under Kernel, click 3×3 Edge Enhance.
3. Click the Edit button in the Kernel Selection box. The 3 × 3 Edge Detect dialog opens.
For this exercise, you use the Kernel Editor to simply view the kernel used for the 3 × 3 Edge Enhance filter. However, if desired, you could make changes to the kernel at this time by editing the Cell Array.
4. Select File Close from the 3 × 3 Edge Enhance dialog.
5. Click OK in the Convolution dialog. A Job Status dialog displays, indicating the progress of the function.
6. When the Job Status dialog shows that the process is 100% complete, click OK.
Check the File
1. Select File Open Raster Layer from the Viewer menu bar. The Select Layer To Add dialog opens.
2. In the Select Layer To Add dialog under Filename, click jaipur.img.
3. Click OK to display the file in the Viewer.
4. Open a second Viewer window by clicking on the Viewer icon on the ERDAS IMAGINE icon panel.
5. Select File Open Raster Layer from the menu bar of the Viewer you just opened. The Select Layer To Add dialog opens.
6. In the Select Layer To Add dialog under Filename, enter the name of the directory in which you saved convolve.img, and press the Enter key on your keyboard.
7. In the list of files, click convolve.img and then click OK. The output file generated by the Convolve function, convolve.img, displays in the second Viewer.
8. In the ERDAS IMAGINE menu bar, select Session Tile Viewers to compare the two files side by side.
Outcome Image
The output of above explained process of spatial enhancement is below
It is clearly visible from enhanced image that vegetation patch (in Red color) appear separately as compared to before image in the left. Same for the built up area (Cyan or bluish color), these patched also visible clearly and separately with the road and nearby patches.
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Thank you sharing Image enhancement technique code !! Useful one !!