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 ma...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 （M 3 DA） for semantic classification for high-resolution satellite images. In our M 3 DA model, the maximum entropy discrimination latent Dirichlet allocation （MedLDA） model is applied to learn the topic-level features first, and then based on a bag-of-words repre- sentation 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.展开更多
基金Supported by the Open Projects Program of National Laboratory of Pattern Recognition and the National Natural Science Foundation of China(91338113)
文摘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 （M 3 DA） for semantic classification for high-resolution satellite images. In our M 3 DA model, the maximum entropy discrimination latent Dirichlet allocation （MedLDA） model is applied to learn the topic-level features first, and then based on a bag-of-words repre- sentation 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.
