I am quite new as I only use google earth engine for a couple weeks. So if I missed anything feel free to advise
The goal want to achieved is to perform time series classification on Google Earth Engine (GEE). So far, I have only seen per image classification in the documents. I also found that you can fit a regression with image collection from Time Series Analysis in Earth Engine By Google Earth. But I can't find a way to combine these two together (I know regression and classification is two different things, but maybe I can extract model from time series analysis and use them with classification?)
If using image based classification alone, it lose the phonological pattern which can indicate plants signature overtime.
Is there a way to perform time series supervised classification on GEE?
Update 1 30/08/19
I have found a way around this by extracting time-series features by first extract interval mean in different range as different bands (From Foyou Tian, et al. ). And then use the aggregated image to classify. However this method did not fully utilize full-extent of timeseries information, and I am looking toward an alternative solution soon
For more complex model, I might use tensorflow based on slide that I have found by Chris Brown from EEIA 2019 here
If I found anything else useful I will keep update, And if anyone have a suggestion, I would gladly accepted
I have recently got back to GEE and I decided to make linear interpolation by answering Smoothing/interpolating across images in an ImageCollection to remove missing data. So with a little bit of modification, I made a simple (pixel-wise) time-series classification of CDL using smile random forest model by flatten all interpolated time-series values into bands of single image. This method, however ignore the time invariant aspect of time-series classification, a better method would be sliding window method.
- Just for baseline benchmark, I’ve got about 70% in class accuracy and 75% in area accuracy (validation) within the same year and region over 34 classes (I think this is not too bad)
- I have tested a correlation between time-series interpolated using savitzky-golay vs linear interpolation in python. The result show high correlation (about
0.89), so I think for most application linear interpolation is sufficient
The code is available here