Classification of GRD product

Microwave Toolbox
#71

I am expecting gpt to do preprocessing work(apply orb file - calb - spk filt - terrain flattening- terrain correction- sigmma0 ) to the raw data. I want this finally generated sigmma0 data to be used in my further analysis/processing.
I wrote a python code to carryout my analysis. It include multi segmentation thresholding approach to generate flood inundation map.
I want the whole process to be atomatic ranging from raw data to flood inundation map generation. But here, my whole work is divided in to two parts one is being carried out in gpt(preprocessing) and other part is being done in python(analysis/processing) . Here, i am facing problem in attaching both of them for continous processing .

#72

You can call the gpt from within python as well

import os

cmd = "gpt preprocessing.xml input_file intermediate_output"
os.system(cmd)

outfile 2 = python.processing(intermediate_output final_output)
1 Like
#73

As @ABraun suggested you can invoke gpt from python as a separate process, but you can also use the API of the Graph Processing Framework.

Some examples can be found here (by @antonio19812 ):

Also these forum threads can be helpful:



#74

In “2. 1. original --> callibrated -> glcm --> terrain correction” can I include terrain flattening after glcm and before terrain correction to improve the accuracy of the classification.

#75
  1. When classifying GRD product using algorithms like maximum likelihood or random forest classifier. Is it necessary to convert the data in to decibels after terrain correction? How does it effect the results ?
  2. Out of sigmma0 and gamma0 product which suits better for generating binary image of water and non-water using Maxinum likelihood, random forest classifier and thresholding technique ?
#76

  1. you can only calculate terrain flattening if you calibrated to beta0.

  2. maximum likelihood is not suitable for radar data because the data is not distributed equally (vh is generally lower than vv), random forest works better. For both cases, conversion to dB is helpful because it changes the histogram [please see the examples here and here]. If you want to include image textures, random forest is necessary because it is based on thresholds and not of clusters. If you don’t want to include textures, please have a look at these comments on feature space here.

  3. Please have a look at the difference between Sigma0 and Gamma0 here. As these refer to topographically induced radiometric distortions (and water bodies are usually flat), it barely makes a difference. Calculate a scatter plot between Sigma0 and Gamma0 to see where they are different.

#77

I am aware of it, but I want know will the accuracy improve if the terrain flattening is done on GLCM products before classification.

#78

Gamma0 is more likely to represent the actual backscatter of a surface (impact of topography is reduced), so basically yes. But for water bodies, the difference will be small.

#79

Hello @ABraun !

import os
cmd = "gpt preprocessing.xml input_file intermediate_output"
os.system(cmd)`

this works perfectly for me . Thank you.
Now, the issue is I am using internet with proxy settings. So, my command prompt is not able to get the internet access. From internet I came to know that we have to give this command to cmd prompt to access internet :

set http_proxy=http://username:password@your_proxy:your_port

When I enter this command in cmd it works fine but i want to pass this from the python code itself as we did for the gpt. How can I achieve this ?

#80

I don’t know because haven’t done this so far, sorry.
Maybe someone other can help.

#81

It’s ok. Thank You.

#82

@SaiKiran
please have a look at this page:
https://senbox.atlassian.net/wiki/spaces/SNAP/pages/30539785/Update+SNAP+from+the+command+line
At the bottom is described how you can configure the proxy without using the SNAP GUI.
If you can use the GUI you can go to Tools / Options in the menu. Switch to the WWW tab and configure the proxy.

Error in GPT while running it from python
#83

hi team,
I’m very sorry for this question,
while doing the supervised classification getting the error as source products are of different dimensions.
following the above discussions but I’m not able to do rectify.

KDtree
KDtree.PNG881×852 227 KB
RF%20clss
RF clss.PNG866×859 331 KB

could you please help me to overcome this.

thank you in advance.

#84

This error is discussed in here, read the post carefully many solutions and suggestions are available

Source of the post

Here also, you could find the step of the classification,

Source of the post

#85

sir from the above he did with sentinel 1 and sentinel 2,
but I’m doing for only 3 sentinel 1 images.

thank you in advance

#86

you have to make sure that all your rasters are in the same coordinate reference system (the one you selected for terrain correction)

You can check using this one grafik if all of them have the same projection information.

#87

As @ABraun mentioned be sure

But in your case I think your input is S-1 GRD, is that right?

In this case your vector, as it is your train on vector, the reference system should be same to S-1 Lan/Long EPSG:4326 WGS84

#88

thank you for your quick response
sir every image in the same coordinate system, because i done the batch processing for these all images.
sorry sir I don’t know hoe to check thisgrafik

#89

thank you for your quick response,

yes sir my all inputs are S-1 GRD.
you mean my input vector also should be in the same coordinate system of S-1 TC products.

#90

Yes, exactly