Figure 1.
Conditional generative countermeasure network structure
Figure 2.
Working principle of marine background condition generated countermeasure network model
Figure 3.
U-net generator network structure and coding and decoding structure diagram
TABLE I
GENERATOR MODEL NETWORK STRUCTURE PARAMETER TABLE
| Inputs | Type | Kernel | Batch Normalization | Activation Function | Outputs |
|---|---|---|---|---|---|
| 256x256 | conv | 4x4 | YES | RELU | 128x128 |
| 128x128 | conv | 4x4 | YES | RELU | 64x64 |
| 64x64 | conv | 4x4 | YES | RELU | 32x32 |
| 32x32 | conv | 4x4 | YES | RELU | 16x16 |
| 16x16 | conv | 4x4 | YES | RELU | 8x8 |
| 8x8 | conv | 4x4 | YES | RELU | 4x4 |
| 4x4 | conv | 4x4 | YES | RELU | 2x2 |
| 2x2 | conv | 4x4 | YES | RELU | 1x1 |
| 1x1 | deconv | 4x4 | YES | RELU | 2x2 |
| 2x2 | deconv | 4x4 | YES | RELU | 4x4 |
| 4x4 | deconv | 4x4 | YES | RELU | 8x8 |
| 8x8 | deconv | 4x4 | YES | RELU | 16x16 |
| 16x16 | deconv | 4x4 | YES | RELU | 32x32 |
| 32x32 | deconv | 4x4 | YES | RELU | 64x64 |
| 64x64 | deconv | 4x4 | YES | RELU | 128x128 |
| 128x128 | deconv | 4x4 | YES | RELU | 256x256 |
TABLE II
DISCRIMINATOR MODEL NETWORK STRUCTURE PARAMETER TABLE
| Inputs | Type | Kernel | Batch Normalization | Activation Function | Outputs |
|---|---|---|---|---|---|
| 256x256 | conv | 4x4 | YES | LeakyReLU | 128x128 |
| 128x128 | conv | 4x4 | YES | LeakyReLU | 64x64 |
| 64x64 | conv | 4x4 | YES | LeakyReLU | 32x32 |
| 32x32 | conv | 4x4 | YES | LeakyReLU | 31x31 |
| 31x31 | conv | 4x4 | YES | LeakyReLU | 30x30 |
Figure 4.
Network structure diagram of discriminator
TABLE III
EXPERIMENTAL ENVIRONMENT
| Operating system | Ubuntu 18.04 LTS 64bit |
|---|---|
| CPU | Intel(R )Xeon(R) Gold 5118 CPU@2.30GHz |
| GPU | Nvidia GeForece TITAN Xp |
| Memory | 32G |
| programing language | Python3.6.1 |
| compiler | Pycharm2018.3 |
| Deep learning framework | pytorch 0.4 |
Figure 5.
Experimental process
Figure 6.
Preprocessing of high resolution ship remote sensing image
Figure 7.
Model training process
Figure 8.
Sample random sample conditional mask
Figure 9.
Generator structure of contrast experiment model
Figure 10.
Residual block network structure in converter
Figure 11.
Random conditional sample generation results