is it possible to use more than three bands in image segmentation? I carried out image segmentation but I feel that the result is not satisfactory. most area of the image represented only one class, built up area and green vegetation represented the rest of the image??
Dear Ahmed, this is a complex question that is hard to answer in a short text. In a nutshell: you can use as many layers/bands as you want, and you can even add non-image layers, such as a DEM or a topo map. Especially using a map is great because it brings in information, such as cadastral or parcel boundaries, or utility lines, that you can't see in images, yet that can play a role when classifying the land. Segmentation is also great when object properties, such as shape, size, location, clustering etc. can help you in identifying what they are (e.g. a small water body of a certain shape may be a swimming pool rather than a natural pond), rather than just have pixels classified as water. However, segmentation can also be tricky where there is poor contrast in your image, the resolution is poor, or when the pixel colors vary a lot (e.g. when you have a roof that is not homogeneous). Then you get poor results.
In my group we wrote a lot of papers using object-based image analysis (OBIA) in relation to hazards - maybe it helps to have a look.
Good luck, Norman
I am not sure I understand the problem...but, yeah, you can have multiple modalities in your segmentation algorithms *depending on the approach obviously*.
Dear Ahmed Ghodieh,
Look the link, maybe useful.
https://pdfs.semanticscholar.org/0cb6/27a165cf837ee147df8127f2065a9ab83dc5.pdf
Article Optimized image segmentation and its effect on classification accuracy
Regards,
Shafagat
Actually how image Segmentation works is find the pixel that corelated area which conducted. and it is easy for machine find the are to classify
Dear Ahmed, this is a complex question that is hard to answer in a short text. In a nutshell: you can use as many layers/bands as you want, and you can even add non-image layers, such as a DEM or a topo map. Especially using a map is great because it brings in information, such as cadastral or parcel boundaries, or utility lines, that you can't see in images, yet that can play a role when classifying the land. Segmentation is also great when object properties, such as shape, size, location, clustering etc. can help you in identifying what they are (e.g. a small water body of a certain shape may be a swimming pool rather than a natural pond), rather than just have pixels classified as water. However, segmentation can also be tricky where there is poor contrast in your image, the resolution is poor, or when the pixel colors vary a lot (e.g. when you have a roof that is not homogeneous). Then you get poor results.
In my group we wrote a lot of papers using object-based image analysis (OBIA) in relation to hazards - maybe it helps to have a look.
Good luck, Norman
You may want to consider the problem from a different perspective.
Image segmentation, is by itself a classification problem. Even though most image segmentation techniques, are understood as simple techniques for separating foreground from background, or to divide the image in non overlapping regions. And classification is more used in machine learning. In reality is only a problem of use as both terms are synonyms.
So yes, image segmentation techniques can improve image classification. What you are probably intending to ask is if the use of different layers of classification techniques can improve your classification results. The answer is yes.
However. I’m a bit unsure if we are thinking in the same thing when speaking about bands. Of course you can use as many bands as you want in your segmentation algorithm, or concatenate different algorithms for different bands, or use a segmentation based on edges, frequencies, or movement.
Dear Ahmed,
As i understand you problem, the classification is not working as it is desired.
Image segmentation is basically finding the relation of group pixels with its neighboring group of pixels in terms of attributed of one group to another. The attributes are decided by application. In your case, the classification divides pixels as build up areas and green vegetation after segmentation of three band image. It means the segmentation is not helping to classified according to your desired types. You can improve this by better selection of attributes of group of pixels.
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