高分辨率遥感影像可以为小型港口的监管提供有效途径.针对小型港口形态多样、特征难以描述等问题，研究了一种基于概率潜在语义分析（probabilistic latent semanticanalysis，PLSA）模型和词袋（bag of words，BoW）模型的小型港口检测方法.该方法首先提取水岸线以缩小搜索范围；然后将灰度直方图、归一化差分水体指数、分形维数特征引入PLSA模型生成特征描述集，将加速鲁棒特征向量引入BoW模型生成视觉词典；根据以上特征描述集和构建的小型港口样本库训练SVM分类器，利用22幅影像进行小型港口检测实验.实验结果表明，相比于只使用常见单一特征或单一模型，该方法的检测结果更佳，耗时更少.
Remotely sensed high resolution imaging is an effective way to monitor small harbors along coastlines. Harbors vary a lot in features and are usually difficult to describe. This paper studies a harbor detection method based on probabilistic latent semantic analysis (PLSA) and bag of words (BoW). Firstly, coastlines are extracted to shrink searching areas. Then grey level histogram, normalized differential water index and fractal dimension features are introduced to PLSA model to generate feature description sets. Eigenvectors of speeded up robust features are introduced to BoW model to generate visual dictionary. Finally, after collecting samples of small harbors, support vector machine (SVM) classifiers are trained based on the above features. Based on the trained classifier, small harbor detection is implemented based on 22 images. Experiments show that the proposed method reaches the best accuracy with relatively low time cost than single-feature models and single PLSA or BoW model.
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