Ship detection from optical remote sensing images based on PLSA model
引入PLSA模型的光学遥感图像舰船检测

Ship detection is one of the important areas in remote sensing applications. However, many ship detection approaches often face a difficult dilemma between low detection rate and high false rate, because of the un-matching between object and its features caused by the complicated characteristics of remote sensing images. Therefore, this paper proposes a novel detection algorithm based on Probabilistic Latent Semantic Analysis (PLSA). It firstly describes the object in terms of the probability combination of latent aspects generated by PLSA, then discriminates the latent aspects model of object by statistics recognition method to obtain the final detection result. The generated latent aspects model represents the joint probability of objects and their features, and gives an explanation for the above un-matching problem by the probability distribution of latent aspects. The performance of the proposed algorithm is demonstrated through the ship detection in various optical remote sensing images, and substantiated using quantitative criteria.