改进的ELU卷积神经网络在SAR图像舰船检测中的应用

随着航天技术的发展，我国SAR载荷的探测体系呈现多种类、多分辨率的发展趋势。传统的检测识别方法很难适应多分辨率、多种类的SAR图像数据，从而需要寻求一种能从多分辨率的图像数据中提取有效特征的方法。智能化发展非常迅速，本文基于SAR图像的特点，提出了改进的ELU激活函数卷积神经网络的方法，建立了结合ELU激活函数和二次代价函数的深度学习模型。同时，在训练样本中建立样本特征与所在分类中心的距离函数，用模糊支持向量机（FSVM）对提取的特征进行了分类。试验结果表明，本文方法提高了SAR图像舰船检测的抗噪性，并且检测率达到了98.6%。

Application of an Improved ELU Convolution Neural Network in the SAR Image Ship Detection

With the development of space technology,the SAR load detection system in our country is showing the development trend of variousness and multi resolution.Since the traditional detection identification methods are difficult to satisfy the multiresolution and various characteristics of SAR image data,it' s necessary to seek a different method to extract effective features from the multiresolution image data F.or the rapidly development of intelligence ,our scheme bases on the characteristics of SAR image proposes the convolution of the improved neural network method and utilizes ELU as an activation function to establish the deep learning model,which combined with the ELU and quadratic cost function.At the same time,based on the training sample, we establish the sample characteristics and centric distance functions,and then use the fuzzy support vector machine to classify the extracted characteristics.The experimental results show that the proposed method can improve the noise resistance of SAR image for the ship detection, and the detection rate can reach up to 98.6％.