Landsat-Time-Series-Analysis-for-Multi-Temporal-Land-Cover-Classification
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20 hours ago •
agr-ayush
agr-ayush
LANDSAT Time Series Analysis for Multi-temporal Land Cover Classification using Machine Learning techniques in Python and GUI development for automation of the process.
Landsat-Time-Series-Analysis-for-Multi-Temporal-Land-Cover-Classification
The Time series analysis for the landsat images was implemented using the random forest machine learning algorithm. The algorithm classified the image into 4 classes:
- Vegetation
- Urban
- Bare Soil
- Water
To do the classification different phases were implemented.
- The first phase includes the calculation of NDVI (Normalized Difference Vegetation Index) and the MNDWI(Modified Normalized Difference Water Index).
- The second phase involves the stacking of all these .tiff files into a single .tiff file. The stacking generally involves the use of bands from 2 to 7, annual NDVI files and the MNDWI file of the selected day.
- Using this Stacked image we predict the classes using our random forest algorithm and classify the images into the above mentioned 4 classes.
agr-ayush