I am new to the concept of supervised classification technique. I am trying to use an SVM classifier for classifying Sea Ice types in the Arctic using satellite image. All the tutorials I have read for performing a supervised SVM classification use data that comes with labels. For instance, if we have a table which is populated with different crops (trying to predict the crop type), each row of the table corresponds to a label (crop type). Then. the procedure for classifying these data is straightforward.
In my case, I have acquired a satellite image over my area of interest. A satellite image has x,y coordinates and two bands (channels). To train my model (using
scikit-learn), I have to provide the SVM classifier with training and target data (which is the label data). In my case, I have no label data.
According to my understanding, I have to create label data somehow out of my whole dataset? Is that right? I am trying to get my head around that problem. Is there any automatic method in python where it trains the model without providing label data? I have not managed to find a solution so far