| Abstract: | The performance of scene classification of satellite images strongly relies on the discriminative power of the low-level and mid-level feature representation. This paper presents a novel approach, named multi-level max-margin analysis(M3DA) for semantic classification for high-resolution satellite images. In our M3 DA model, the maximum entropy discrimination latent Dirichlet allocation(Med LDA) model is applied to learn the topic-level features first, and then based on a bag-of-words representation of low-level local image features, the large margin nearest neighbor(LMNN) classifier is used to optimize a multiple soft label composed of word-level features(generated by SVM classifier) and topic-level features. The categorization performances on 21-class land-use dataset have demonstrated that the proposed model in multi-level max-margin scheme can distinguish different categories of land-use scenes reasonably. |
