Classification of Summer Crops Using Active Learning Techniques on Landsat Images in the Northwest of the Province of Buenos Aires

Abstract

The present work aims to obtain a classifier for summer crops in the northwest of Buenos Aires province from Landsat satellite images. Active Learning (AL) was used as the classification technique since it obtains satisfactory results using a small set of labeled samples to train the algorithm. The construction of the training set is iteratively performed by means of a heuristic for the selection of the unlabeled samples to be classified by an expert. The following heuristics were used for comparison: Breaking Ties, Multiclass Level Uncertainty, Margin Sampling, and Random Sampling. The algorithm was also compared with the supervised technique Support Vector Machine (SVM). The experiments were tested on three Landsat 8 images from different dates using 6 bands per image and various vegetation indices. The results obtained using AL in combination with the different heuristics do not differ substantially from SVM.