Abstract
To maintain national socio-economic development and maritime rights and interests, it is necessary to obtain the space location information of various ships. Therefore, it is important to detect the locations of ships accurately and rapidly. At present, ship detection is mainly carried out by combining satellite remote sensing imaging with constant false alarm rate (CFAR) detection. However, with the rapid development of satellite remote sensing technology, remote sensing data have gradually begun to show the characteristics of “big data”; additionally, the accuracy and speed of ship detection can be improved by analysing big data, such as by deep learning. Thus, a ship detection algorithm that combines CFAR and CNN is proposed based on the CFAR global detection algorithm and image recognition with the CNN model. Compared with the multi-level CFAR algorithm that is based on multithreading, the algorithm in this paper is more suitable for application to ship detection systems.
Highlights
With the development of marine exploration, marine transportation and exploitation of submarine resources, activities of ships are becoming increasingly frequent; safety requirements for marine transportation are increasing
With the rapid development of satellite remote sensing technology, remote sensing data have gradually begun to show the characteristics of ‘‘big data’’; the accuracy and speed of ship detection can be improved by analysing big data, such as by deep learning
A ship detection algorithm that combines constant false alarm rate (CFAR) and convolutional neural networks (CNNs) is proposed based on the CFAR global detection algorithm and image recognition with the CNN model
Summary
With the development of marine exploration, marine transportation and exploitation of submarine resources, activities of ships are becoming increasingly frequent; safety requirements for marine transportation are increasing. CNN is a widely used model and method in deep learning, which is developed based on an artificial neural network It is composed of multiple processing layers to learn representations of data with multiple levels of abstraction (Xu and Scott 2017). To improve the accuracy of maritime SAR image detection, in this paper, CFAR global detection and the CNN model are used for ship detection. The overall design process consists of the following steps: First, obtain the training and validation samples of the ship and construct a CNN optimal network model with small error rate. Local detection is performed by using the trained CNN optimal model to obtain the classification results of ship and sea clutter. When all pixels are compared, all target pixels that are different from the surrounding clutter points in the SAR image are detected, and the index matrix is obtained
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