Task: Determining different crop types from satellite imagery.
Tools at hand
- Training data set which includes RGB, longitude and latitude, NDVI and crop type for each pixel (10m by 10m)
- Test data set containing RGB, longitude and latitude, NDVI values but no crop type.
- Data from Sentinel-2
- I do not know all crop types I am trying to identify. As an example, my training data-set may include 2 different crop types (tomatoes and potatoes), but I may come across a different crop in my test-data set which was not seen during training but I need to be able to correctly identify that a new crop was detected.
I need to choose the correct statistical learning model to correctly predict or identify (not sure which is the correct term to use) different crop types from my test data set.
I am not certain as to what type of statistical learning methodology should be used in this case.
On the one hand I am trying to predict a categorical or qualitative output (classification) but on the other hand I am also predicting similarities (clustering).
- Linear Classifiers: Logistic Regression, Naive Bayes Classifier
- Support Vector Machines
- Decision Trees
- Boosted Trees
- Random Forest
- Nearest Neighbor
- Mean Shift
Above I have listed some of the most popular algorithms of each category.
I am also aware (see supervised vs unsupervised classification in identification of region of interest) that if this is indeed a classification problem, I need to decide whether a supervised or unsupervised approach should be used.
I am looking for a high-level approach to such a task with explanations as to why this particular approach was suggested, and why the algorithms chosen are a better choice than others.
For example, should cluster analysis be performed followed by classification, or would classification without clustering be adequate? If classification alone is adequate, which algorithms should be considered and why?