Improving high-resolution satellite images retrieval using Linear SVM classifier and data augmentation

摘要

The availability of huge remote sensing image dataset imposes recourse to powerful techniques of content-based image retrieval for archiving and mining. This paper propose descriptors based on the SIFT (Scale invariant features) combined with SVM linear classification. To build a powerful image classifier using very little training data, image augmentation is usually required to boost the performance of the classification. For this reason, an augmentation data is used to increase the training data for the SVM (Support vector Machine) classifier. The creation of the training data is done using several techniques of augmentation: anisotropic filter. We report a first evaluation of the CBIR (Content based image retrieval) and the second evaluation of the system aims to compare the deep learning with the boosted SVM classification.