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Many ship target detection methods have been developed since it was verified that ship could be imaged with the space-based SAR systems. Most developed detection methods mostly emphasized ship detection rate but not computation time. By making use of the advantages of the K-distribution CFAR method and two-parameter CFAR method, a new CFAR ship target detection algorithm was proposed. In that new method, we use the K-distribution CFAR method to calculate a global threshold with a certain false-alarm rate. Then the threshold is applied to the whole SAR imagery to determine the possible ship target pixels, and a binary image is given as the preliminary result. Mathematical morphological filter are used to filter the binary image. After that step, we use the two-parameter CFAR method to detect the ship targets. In the step, the local sliding window only works in the possible ship target pixels of the SAR imagery. That step avoids the statistical calculation of the background pixels, so the method proposed can much improve the processing speed. In order to test the new method, two SAR imagery with different background were used, and the detection result shows that that method can work well in different background circumstances with high detection rate. Moreover, a synchronous ship detection experiment was carried out in Qingdao port in October 28, 2005 to verify the new method and one ENVISAT ASAR imagery was acquired to detect ship targets. It can be concluded from the experiment that the new method not only has high detection rate, but also is time-consuming, and is suitable for the operational ship detection system.

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High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.such as the sea clutter, the ionospheric clutter and the radio frequency interference (RFI) severely limit the detection ability of HFSWR. Hence, the detection and suppression of the clutter [7] and the interference [8,9] is essential to guarantee the performance of HFSWR, the automatic and accurate detection in particular is a prerequisite for the suppression of the clutter. Although the suppression of clutter and interference can be implemented without prior detection, both theoretical and simulation results show that the signal to noise ratio (SNR) of the target also can be obviously reduced after implementing clutter/interference suppression [10]. Hence, it is of great importance to detect whether there is existence of clutter/interference before initiating clutter/interference suppression for reservation of the signal energy and improvement of computation efficiency.The most common ways to identify and detect the clutter and the interference based on Range-Doppler (RD) spectral image are usually divided into two parts: The first step is to extract features from the images; then use the traditional image segmentation techniques to achieve the goal or use the machine learning which is currently the cutting-edge technology in the field of identifying and detecting objects. Chen et al. [11] used the corresponding analysis and cluster analysis methods to classify interfe...