A sea–land clutter classification framework for over-the-horizon radar based on weighted loss semi-supervised generative adversarial network
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 …
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 …
A sea-land clutter classification framework for over-the-horizon-radar based on weighted loss semi-supervised gan
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 …
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 …
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