轻量化卷积神经网络在SAR图像语义分割中的应用

针对合成孔径雷达图像的语义分割问题,构建了一个全新的TerraSAR-X语义分割数据集GDUT-Nansha。然后,为解决传统深度学习方法模型体积大,难以在样本数量偏少的合成孔径雷达图像数据集上应用的问题,对轻量化卷积神经网络ENet模型进行了分析和改造。提出了一种改进的轻量化卷积神经网络模型(revised weighted loss eNet,RWL-ENet);针对合成孔径雷达图像数据集样本不平衡问题,使用了带有权重的损失函数。通过和其他经典卷积神经网络语义分割模型的对比实验,验证了新数据集的可靠性;同时,在参数量和模型体积远远小于其他网络模型的前提下,RWL-ENet模型在像素精度、平均像素精度、平均交并比三个定量指标上分别达到了0.884、0.804和0.645。

Application of lightweight convolutional neural network in SAR image semantic segmentation

This paper constructed a new TerraSAR-X dataset named GDUT-Nansha and then proposed a new lightweight semantic segmentation algorithm using synthetic aperture radar images(SAR).It is difficult to apply traditional deep learning models to SAR datasets with small volume due to the tremendous amount of parameters.A revision scheme is,therefore,put forward to deal with such problem.This paper accomplished an improved lightweight convolutional neural network model named revised weighted loss ENet(RWL-ENet)for SAR images.The current study introduced a weighted loss function to solve the problem of imbalance of training datasets.Compared with other classical convolution neural network models,the efficiency and robustness of the new dataset were validated.Meanwhile,RWL-ENet model attained 0.884,0.804,and 0.645 in terms of three quantitative metrics,including pixel accuracy(PA),mean pixel accuracy(mPA),and mean intersection over union(mIoU).In addition,the parameters of this new proposed model are much less than other classic network models.