Bag-of-Visual Words and Error-Correcting Output Codes for Multilabel Classification of Remote Sensing Images

This paper presents a novel framework for multilabel classification of remote sensing images using Error-Correcting Output Codes (ECOC). Starting with a set of primary class labels, the proposed framework consists in transforming the multiclass problem into binary learning subproblems. The distributed output representations of these binary learners are then transformed into primary class labels. In order to obtain robustness with respect to scale, rotation and image content, a Bag-of-Visual Words (BOVW) model based on Scale Invariant Feature Transform (SIFT) descriptors is used for feature extraction. BOVW assumes an a-priori unsupervised learning of a dictionary of visual words over the training set. Experiments are performed on GeoEye-1 images and the results show the effectiveness of the proposed approach towards multilabel classification, if compared to other methods.