Deep convolutional neural network has made great achievements in sea-land clutter classification for over-the-horizon-radar (OTHR). The premise is that a large number of labeled training samples must be provided for a sea-land clutter classifier. In practical engineering applications, it is relatively easy to obtain label-free sea-land clutter samples. However, the labeling process is extremely cumbersome and requires expertise in the field of OTHR. To solve this problem, we propose an improved generative adversarial network, namely weighted loss semi-supervised generative adversarial network (WL-SSGAN). Specifically, we propose a joint feature matching loss by weighting the middle layer features of the discriminator of semi-supervised generative adversarial network. Furthermore, we propose the weighted loss of WL-SSGAN by linearly weighting standard adversarial loss and joint feature matching loss. The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset. The experimental results show that WL-SSGAN can improve the performance of the fully supervised classifier with only a small number of labeled samples by utilizing a large number of unlabeled sea-land clutter samples. Further, the proposed weighted loss is superior to both the adversarial loss and the feature matching loss. Additionally, we compare WL-SSGAN with conventional semi-supervised classification methods and demonstrate that WL-SSGAN achieves the highest classification accuracy.

提出了一种改进的生成对抗网络——加权损失半监督生成对抗网络(WL-SSGAN)，通过对半监督生成对抗网络的判别器中间层特征进行加权处理，提出了联合特征匹配损失的联合，通过线性权重提出了WL-SSGAN的加权损失，实验结果表明，在只使用极少量有标签样本的情况下，WL-SSGAN可以通过利用大量无标注海陆地杂波样本来提高完全监督分类器的性能，并且WL-SSGAN的加权损失优于对抗损失和特征匹配损失。

基于加权损失半监督GAN的海陆杂波分类框架