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Classification model using NDVI time series?
The 2019 Stack Overflow Developer Survey Results Are InSPOT NDVI time seriesTIMESAT input image requirement?Can you apply Maximum Likelihood classification to NDVI?Random Forest classification not in RMonthly GIMMS NDVI time seriesHow should I fill gaps in an NDVI time series?How can I fill NA values in my NDVI Data?Analyze time series NDVI using bfastChoosing statistical method for determining different crop types?Error in smoothing NDVI (BISE) using SPIRITS
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I am trying to form a classification model with the help of NDVI time series data. I want to use methods like SVM and Random Forest. I know how to apply these methods to normal data. But, I am confused while applying it to this time series data.
This is a sample of my data:
Here the data have the different type of crops as classes. After that, each column represents the number of days after sowing.
I was thinking of taking these columns i.e. number of days as my random variables and Class as my response variable to apply random forest and SVM classifiers. But I have a doubt if we can do that?? Is it right to apply classification method or there is some other procedure to do it on this type of data?? Please suggest me how can I apply classification method?? Any help would be highly valuable for me.
classification ndvi land-classification random-forest
asked May 25 '18 at 6:10
agangwalagangwal
132
add a comment |
I am trying to form a classification model with the help of NDVI time series data. I want to use methods like SVM and Random Forest. I know how to apply these methods to normal data. But, I am confused while applying it to this time series data.
This is a sample of my data:
Here the data have the different type of crops as classes. After that, each column represents the number of days after sowing.
I was thinking of taking these columns i.e. number of days as my random variables and Class as my response variable to apply random forest and SVM classifiers. But I have a doubt if we can do that?? Is it right to apply classification method or there is some other procedure to do it on this type of data?? Please suggest me how can I apply classification method?? Any help would be highly valuable for me.
classification ndvi land-classification random-forest
asked May 25 '18 at 6:10
agangwalagangwal
132
You can apply any machine learning model, I don't see the problem of using NDVI columns as predictors. Just for curiosity... Why are NDVI values of 20 days after sowing bigger than NDVI values of 40 days?
– aldo_tapia
May 25 '18 at 11:22
The ndvi values are calculated from optical data. So there may be some atmospheric condition on that day which altered the actual ndvi values.
– agangwal
May 25 '18 at 12:12
add a comment |
I am trying to form a classification model with the help of NDVI time series data. I want to use methods like SVM and Random Forest. I know how to apply these methods to normal data. But, I am confused while applying it to this time series data.
This is a sample of my data:
Here the data have the different type of crops as classes. After that, each column represents the number of days after sowing.
I was thinking of taking these columns i.e. number of days as my random variables and Class as my response variable to apply random forest and SVM classifiers. But I have a doubt if we can do that?? Is it right to apply classification method or there is some other procedure to do it on this type of data?? Please suggest me how can I apply classification method?? Any help would be highly valuable for me.
classification ndvi land-classification random-forest
asked May 25 '18 at 6:10
agangwalagangwal
132
I am trying to form a classification model with the help of NDVI time series data. I want to use methods like SVM and Random Forest. I know how to apply these methods to normal data. But, I am confused while applying it to this time series data.
This is a sample of my data:
Here the data have the different type of crops as classes. After that, each column represents the number of days after sowing.
I was thinking of taking these columns i.e. number of days as my random variables and Class as my response variable to apply random forest and SVM classifiers. But I have a doubt if we can do that?? Is it right to apply classification method or there is some other procedure to do it on this type of data?? Please suggest me how can I apply classification method?? Any help would be highly valuable for me.
classification ndvi land-classification random-forest
classification ndvi land-classification random-forest
asked May 25 '18 at 6:10
agangwalagangwal
132
asked May 25 '18 at 6:10
agangwalagangwal
132
asked May 25 '18 at 6:10
agangwalagangwal
132
asked May 25 '18 at 6:10
agangwalagangwal
132
asked May 25 '18 at 6:10
agangwalagangwal
132
132
You can apply any machine learning model, I don't see the problem of using NDVI columns as predictors. Just for curiosity... Why are NDVI values of 20 days after sowing bigger than NDVI values of 40 days?
– aldo_tapia
May 25 '18 at 11:22
The ndvi values are calculated from optical data. So there may be some atmospheric condition on that day which altered the actual ndvi values.
– agangwal
May 25 '18 at 12:12
add a comment |
You can apply any machine learning model, I don't see the problem of using NDVI columns as predictors. Just for curiosity... Why are NDVI values of 20 days after sowing bigger than NDVI values of 40 days?
– aldo_tapia
May 25 '18 at 11:22
The ndvi values are calculated from optical data. So there may be some atmospheric condition on that day which altered the actual ndvi values.
– agangwal
May 25 '18 at 12:12
You can apply any machine learning model, I don't see the problem of using NDVI columns as predictors. Just for curiosity... Why are NDVI values of 20 days after sowing bigger than NDVI values of 40 days?
– aldo_tapia
May 25 '18 at 11:22
You can apply any machine learning model, I don't see the problem of using NDVI columns as predictors. Just for curiosity... Why are NDVI values of 20 days after sowing bigger than NDVI values of 40 days?
– aldo_tapia
May 25 '18 at 11:22
The ndvi values are calculated from optical data. So there may be some atmospheric condition on that day which altered the actual ndvi values.
– agangwal
May 25 '18 at 12:12
The ndvi values are calculated from optical data. So there may be some atmospheric condition on that day which altered the actual ndvi values.
– agangwal
May 25 '18 at 12:12
add a comment |
2 Answers
2
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oldest
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If my understanding of your question is correct, I see no problem with applying a random Forest, SVM or any other classification method. Class – like you said – is your response variable, the others are predictors. While in your example the predictors may have been created using time series analysis, there is no technical difference between this set of predictors and others that e.g. are rather based on reflectance values in different bands of a single Landsat/Sentinel/Modis scene or any other data. However, be aware that there is no guarantee of an actual connection between the crop type and your predictors; you’ll only be able to examine this after fitting the model.
Just a small annotation: I guess it would be feasible to remove the unnamed: 0 column, as it has no predictive meaning and may bias the result.
answered May 25 '18 at 8:03
bagadoshbagadosh
287
add a comment |
Try KNN with metric as DTW(Dynamic Time warping). It gives good result based on the time-series data.
Source: https://tslearn.readthedocs.io/en/latest/variablelength.html#supervised-classification
answered 22 mins ago
user2447587user2447587
1
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
If my understanding of your question is correct, I see no problem with applying a random Forest, SVM or any other classification method. Class – like you said – is your response variable, the others are predictors. While in your example the predictors may have been created using time series analysis, there is no technical difference between this set of predictors and others that e.g. are rather based on reflectance values in different bands of a single Landsat/Sentinel/Modis scene or any other data. However, be aware that there is no guarantee of an actual connection between the crop type and your predictors; you’ll only be able to examine this after fitting the model.
Just a small annotation: I guess it would be feasible to remove the unnamed: 0 column, as it has no predictive meaning and may bias the result.
answered May 25 '18 at 8:03
bagadoshbagadosh
287
add a comment |
If my understanding of your question is correct, I see no problem with applying a random Forest, SVM or any other classification method. Class – like you said – is your response variable, the others are predictors. While in your example the predictors may have been created using time series analysis, there is no technical difference between this set of predictors and others that e.g. are rather based on reflectance values in different bands of a single Landsat/Sentinel/Modis scene or any other data. However, be aware that there is no guarantee of an actual connection between the crop type and your predictors; you’ll only be able to examine this after fitting the model.
Just a small annotation: I guess it would be feasible to remove the unnamed: 0 column, as it has no predictive meaning and may bias the result.
answered May 25 '18 at 8:03
bagadoshbagadosh
287
add a comment |
If my understanding of your question is correct, I see no problem with applying a random Forest, SVM or any other classification method. Class – like you said – is your response variable, the others are predictors. While in your example the predictors may have been created using time series analysis, there is no technical difference between this set of predictors and others that e.g. are rather based on reflectance values in different bands of a single Landsat/Sentinel/Modis scene or any other data. However, be aware that there is no guarantee of an actual connection between the crop type and your predictors; you’ll only be able to examine this after fitting the model.
Just a small annotation: I guess it would be feasible to remove the unnamed: 0 column, as it has no predictive meaning and may bias the result.
answered May 25 '18 at 8:03
bagadoshbagadosh
287
If my understanding of your question is correct, I see no problem with applying a random Forest, SVM or any other classification method. Class – like you said – is your response variable, the others are predictors. While in your example the predictors may have been created using time series analysis, there is no technical difference between this set of predictors and others that e.g. are rather based on reflectance values in different bands of a single Landsat/Sentinel/Modis scene or any other data. However, be aware that there is no guarantee of an actual connection between the crop type and your predictors; you’ll only be able to examine this after fitting the model.
Just a small annotation: I guess it would be feasible to remove the unnamed: 0 column, as it has no predictive meaning and may bias the result.
answered May 25 '18 at 8:03
bagadoshbagadosh
287
answered May 25 '18 at 8:03
bagadoshbagadosh
287
answered May 25 '18 at 8:03
bagadoshbagadosh
287
answered May 25 '18 at 8:03
bagadoshbagadosh
287
287
add a comment |
add a comment |
Try KNN with metric as DTW(Dynamic Time warping). It gives good result based on the time-series data.
Source: https://tslearn.readthedocs.io/en/latest/variablelength.html#supervised-classification
answered 22 mins ago
user2447587user2447587
1
New contributor
user2447587 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
Try KNN with metric as DTW(Dynamic Time warping). It gives good result based on the time-series data.
Source: https://tslearn.readthedocs.io/en/latest/variablelength.html#supervised-classification
answered 22 mins ago
user2447587user2447587
1
New contributor
user2447587 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
Try KNN with metric as DTW(Dynamic Time warping). It gives good result based on the time-series data.
Source: https://tslearn.readthedocs.io/en/latest/variablelength.html#supervised-classification
answered 22 mins ago
user2447587user2447587
1
New contributor
user2447587 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Try KNN with metric as DTW(Dynamic Time warping). It gives good result based on the time-series data.
Source: https://tslearn.readthedocs.io/en/latest/variablelength.html#supervised-classification
answered 22 mins ago
user2447587user2447587
1
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user2447587 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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answered 22 mins ago
user2447587user2447587
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answered 22 mins ago
user2447587user2447587
1
answered 22 mins ago
user2447587user2447587
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New contributor
user2447587 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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You can apply any machine learning model, I don't see the problem of using NDVI columns as predictors. Just for curiosity... Why are NDVI values of 20 days after sowing bigger than NDVI values of 40 days?
– aldo_tapia
May 25 '18 at 11:22
The ndvi values are calculated from optical data. So there may be some atmospheric condition on that day which altered the actual ndvi values.
– agangwal
May 25 '18 at 12:12