Multi-feature probability topic scene classifier for high spatial resolution remote sensing imagery

Scene classification can obtain the high-level semantic information in high spatial resolution (HSR) imagery. Probability topic model as a typical scene semantic representation has been successfully applied to nature scene by utilizing a single feature. However, it is not completely fit for HSR images due to the complexity of land cover classes. To solve the problem, multi-feature probability topic scene classifier based on Latent Dirichlet allocation (LDA), namely MFPTSC, is proposed for HSR imagery. In MFPTSC, the spectral, texture, and SIFT features as three representative features are firstly integrated. If the traditional multi-features fusion method (VIS-LDA) is used, which each feature vector is usually stacked at the visual word level, abundant information is lost, which leads to an undesirable classification performance. In this paper, a novel feature fusion strategy at the semantic allocation level, named SAL-LDA, is proposed to avoid information loss to a large extent by mining the latent semantics in accordance with the distinctive characteristics of each feature. Experiment results using the image dataset of 21 land-use classes demonstrate that the multi-feature fusion strategies of VIS-LDA and SAL-LDA both improve the classification accuracy, but the proposed SAL-LDA strategy is better than VIS-LDA.