A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images

NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA). The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset

Ship Classification Based on MSHOG Feature and Task-Driven Dictionary Learning with Structured Incoherent Constraints in SAR Images

In this paper, we present a novel method for ship classification in synthetic aperture radar (SAR) images. The proposed method consists of feature extraction and classifier training. Inspired by SAR-HOG feature in automatic target recognition, we first design a novel feature named MSHOG by improving SAR-HOG, adapting it to ship classification, and employing manifold learning to achieve dimensionality reduction. Then, we train the classifier and dictionary jointly in task-driven dictionary learning (TDDL) framework. To further improve the performance of TDDL, we enforce structured incoherent constraints on it and develop an efficient algorithm for solving corresponding optimization problem. Extensive experiments performed on two datasets with TerraSAR-X images demonstrate that the proposed method, MSHOG feature and TDDL with structured incoherent constraints, outperforms other existing methods and achieves state-of-art performance