Abstract: Traditional methods of remote sensing image retrieval cannot handle high-resolution images with huge amounts of surface feature types and complex relations. It requires high cost of time and labor, gives sketchy content description, and cannot adequately consider the semantic information. Inspired by text information retrieval, the visual features in computer vision and the probabilistic topic model in natural language processing are introduced to present a method of retrieving high-resolution remote sensing image based on LDA (Latent Dirichlet Allocation). Retrieval experiments on high resolution remote sensing images with multiple topic numbers suggest that even when the number of topic is small, good retrieval results and high precision can be achieved. As the topic number increases, the precision rate remains at about 0.9.

Key words: LDA, high-resolution remote sensing image, remote sensing image retrieval, visual features