A Semisupervised Latent Dirichlet Allocation Model for Object-Based Classification of VHR Panchromatic Satellite Images

Li Shen¹, Hong Tang¹, Yunhao Chen¹, Adu Gong¹, Jing Li¹, and Wenbin Yi²

¹ State Key Laboratory of Earth Surface Processes and Resource Ecology and the Key Laboratory of Environment Change and Natural Disaster, Beijing Normal University, Beijing 100875, China;

² HSE Information Center, CNPC Institute of Safety and Environment Technology, Beijing 102206, China.

Abstract: Typically, object-based classification methods are learned using training samples with labels attached to image objects. In this letter, a semisupervised object-based method in the framework of topic modeling is proposed to classify very high resolution panchromatic satellite images using partially labeled pixels. In the stage of training, both topics and their co-occurred distributions are learned in an unsupervised manner from segmented satellite images. Meanwhile, unlabeled pixels are allocated user-provided geo-object class labels based on the learned model. In the stage of classification, each segment is classified as a user-provided geo-object class label with the maximum posterior probability. Experimental results show that the proposed method outperforms several SVM-based supervised classification methods in terms of both spatial consistency and semantic consistency.

Keywords: Object-based image analysis; probabilistic topic models; semisupervised image classification.