Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Lingling Li¹  Yake Zhang¹  Licheng Jiao¹  Meijuan Yang²  Fang Liu²  Shuyuan Yang² 

[1] Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education, and the Joint International Research Laboratory of Intelligent Perception and Computation, International Research Center for Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi&x2019;an, China;

关键词: Image classification;    local image manifold;    probabilistic topic model;    sketch structural feature;    synthetic aperture radar (SAR) image;   

DOI  :  10.1109/JSTARS.2020.3024002

来源: DOAJ

【 摘 要 】

Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Each patch in SAR images has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this article proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then, the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. Finally, each image patch is quantified by discriminative latent semantic topics instead of low-level representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.

【 授权许可】

Unknown