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 ?
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.
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 this
Thank you for your good response
I got the solution for that but, While doing the RF classification for the GRD products getting the error like bound must me positive, sorry I don’t know what it is
and also sorry for my lack of knowledge, I have one doubt the classification which we are doing is on what based will be classify?
is it color based or backscatter based? please give me some guidance.
radar data is not measurung colours, it operates at higher wavelenghts. The signal you retrieve (and the classification is based on) represents physical characteristics of the surfaces (roughness and moisture, among others). Rice, water and built-up structures have different types of backscatter mechanisms, so they should be separable quite well. Build-up structures cause corner reflection (very high backscatter), water is mostly smooth and rice produces volume scattering once crops are developed. Especially if you are using images of different dates, its signature will change and be therefore quite unique.
By default the “number of training samples” is 5000 in any classifier in SNAP. How does it effect the results exactly and if I increase it’s value, will the accuracy improve ?
hi team,
sorry for lack of knowledge,as per my concern while we are doing the classification by any technique, we need take vector samples, then only we can classify our products.
is there any technique to classify the products by giving the back-scatter ranges (like -14 db to -19 db & -24 db to -30 db ) only based on our requirement without giving any vector samples??
if any technique is there please help me to classify the products.
and one more question is after done the classification how can we see the statistics based on area wise.
please help me to do that process.