Classification detection method for marine targets in SAR image based on convolution neural network

Abstract

The invention discloses a classification detection method for marine targets in an SAR image based on a convolution neural network, which comprises the steps of first, converting an SAR single-look complex image containing marine targets into a power image, and carrying out quantization on the power image; second, segmenting the quantized power image, adding sample category labels, then constructing a training set, and establishing a data set; third, performing mean removing processing on the training set; fourth, building a convolution neural network model; fifth, training the convolution neural network model by using the training set; and sixth, inputting a slice to be classified into the convolution neural network model to obtain a classification detection result. The classification detection method has the characteristics of high reliability, strong generalization ability, low computational complexity, high practicability, wide application range and the like.

Description

technical field [0001] The invention relates to a method for classifying and detecting marine targets in SAR images based on a convolutional neural network, belonging to the fields of image processing and computer vision. Background technique [0002] Synthetic aperture radar originated in military applications and was developed in the 1950s as an active earth observation system. Synthetic aperture radar can conduct all-weather and all-weather observations of the earth without being affected by climate, weather, light and other conditions. It is widely used in: agriculture, soil moisture, forestry, geology, hydrology, flood and ocean monitoring, oceanography, Ship and oil slick detection, ice and snow detection, land cover mapping, altitude mapping, and earth change detection. [0003] In the past ten years, the convolutional neural network has been greatly developed. The convolutional neural network integrates feature extraction, feature selection, and feature classificati...

AI Technical Summary

Problems solved by technology

Many marine target classification algorithms based on convolutional neural networks have a small number of sample categories and samples, and the network structure is unreasonable, resulting in low practicability of the algorithm and poor classification effect

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