A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar
  Based on Weighted Loss Semi-supervised GAN
Xiaoxuan Zhang Zengfu Wang Kun Lu Quan Pan Yang Li
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.