The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

中文翻译：

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基于机器学习的船舶检测和跟踪使用卫星图像进行海上监视

最近遥感技术的进步导致了不同成像模式和更高分辨率卫星图像的可用性。这些遥感或卫星图像的可访问性、船舶自动检测和跟踪已成为海上监视领域的重要研究课题。在本文中，提出了一种利用卫星图像进行船舶检测的新方法。首先进行预处理以使用船舶检测和跟踪 (SDT) 滤波器从图像中去除噪声。然后，基于使用 SDT 边缘检测的梯度特征提取以及 SDT 分割进行陆地掩蔽（海陆区域分离）和云掩蔽。最后，使用机器学习 (ML) 分类器识别船舶，如支持向量机 (SVM)、随机森林分类器 (RFC)、基于从定向梯度直方图 (HOG) 中提取的特征的线性判别分析 (LDA)、逻辑回归 (LR)、KNN 和基于高斯朴素贝叶斯的分类器。拟议的工作使用谷歌地球数据进行交叉验证。我们提出的方法的性能是使用召回率和精度值来评估的。此外，对于跟踪船舶，使用 Kaggle 数据集提出并测试了改进的多假设跟踪 (MHT) 算法。