OTD_Loglogistic-based SAR (Synthetic Aperture Radar) data ocean target detection method

Abstract

The invention discloses an OTD_Logistic-based SAR data ocean target detection method, and belongs to the field of ocean target detection. The detection method combining initial detection based on a deep learning model and CFAR fine detection based on Logistic is adopted, firstly, an OceanTDA9 lightweight deep learning model is constructed, initial detection of an ocean target is carried out based on the deep learning model, on the basis, a CFAR method based on a Logistic model is adopted to carry out ocean target fine detection, and ocean target features are extracted. According to the method, the ocean target initial detection based on deep learning and the CFAR method based on Logistic are combined, the defects that the calculation cost is long due to the fact that pixels in the image need to be counted one by one through sliding window CFAR, the pixels at the edge of the image cannot be processed, and missing detection at the edge is caused are overcome, and the detection efficiency and precision of the ocean target are improved.

Description

technical field [0001] The invention belongs to the field of ocean target detection, and in particular relates to an OTD_Loglogistic-based SAR data ocean target detection method. Background technique [0002] In recent years, neural networks have been applied to marine target detection, but deep neural networks have the defect of dimensionality disaster, which will reduce the detection speed. Ocean clutter on a calm sea surface can be modeled approximately using a Gaussian model. However, under complex sea conditions such as wind, waves, and tides, the SAR backscatter probability distribution has a long tail and no longer conforms to the Gaussian distribution. The traditional CFAR algorithm is based on the assumption that sea clutter in SAR data obeys a Gaussian distribution, but in complex sea conditions, the distribution of sea clutter is extremely complex, and this assumption is not scientific and universal. Moreover, CFAR needs to count the pixels in the image one by on...

AI Technical Summary

Problems solved by technology

Ocean clutter on a calm sea surface can be approximated using a Gaussian model. However, under complex sea conditions such as wind, waves, and tides, the SAR backscatter probability distribution has a long tail and no longer conforms to the Gaussian distribution.

The traditional CFAR algorithm is based on the assumption that sea clutter in SAR data obeys a Gaussian distribution, but in complex sea conditions, the distribution of sea clutter is extremely complex, and this assumption is not scientific and universal

Moreover, CFAR needs to count the pixels in the image one by one, and the calculation takes a long time. The sliding window cannot process the pixels on the edge of the image, which will cause the defect of missing detection at the edge.

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