A comparison of Bag-of-Words method and normalized compression distance for satellite image retrieval

Recently, two improved methods have shown their advantages in browsing Earth Observation (EO) dataset. The first method is the Bag-of-Words (BoW) feature extraction method and the second is the Normalized Compression Distance (NCD) for assessing image similarity. However, they have not been compared so far for satellite image retrieval, which motivates this paper. Two retrieval experiments have been performed on a freely available optical image dataset and a SAR image dataset. Through these two experiments, we conclude that the BoW method performs generally better than NCD. Although it is a parameter-free solution for data mining, NCD only performs well for images with repetitive patterns like some homogeneous classes. In contrast, BoW method performs much far beyond that of NCD. In addition, NCD is computationally very expensive, which makes it infeasible to be applied in real applications. In contrast, BoW method is more realistic in practical applications in terms of both accuracy and computation.