# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 6 Results
### Comparison of the Model Fitting  
Due to the vast dataset, the models were typically trained for two epochs (split into multiple sub-epochs for better performance monitoring). While less than an epoch would often suffice, no overtraining occurred due to almost no image repetition. As a comparison of these model, Tables 1 and 2 with performance metrics are juxtaposed. Both tables use the same coloring, whereby the green cell represents better metrics (with/without filter pair), and gray numbers underline that the neural network was NOT trained for this operation.  
The values in Table 2 present a simple difference between average metric output and the same metric for images with Gaussian noise. The lower the values, the better the result. The output values can be negative, representing occurrence, where noise improves the metrics (this can occur for poorly trained networks).  
For time measurement, a computer with Intel Xeon W-2133 CPU was used (3.6 GHz, 12 Cores).  
Tables 1 and 2 indicate the high metrics advantage of the Neural Networks trained with filtered labels over simple autoencoders. The advantage is very interesting considering that in all tests, the metrics values were lower (better) for filter DNN, even where unfiltered images were compared. This might be the case due to a higher needed for \"understanding\" of the image (the greater neighboring area has to be considered for pixel value decoding) and better normalization.  
Another interesting note is that the noise actually has a greater influence on filtered images (Table 2), which lowers the convergence of the learning. This should not result in divergent learning but expends the time needed for learning.  
In both tables, an additional CNN with SSIM metric was attached for comparison purposes. As it can be seen, SSIM performed poorly with the SAR imagery.  
Example result images for two networks learning with different labels are shown in Figure 7. The first row presents original data put as input to the neural networks. The \"Fully CNN--Without filters\" replicates the input very accurately. Some minor artefacts can be seen, but it is hard to distinguish this from the original imagery. The \"CompressingCNN--Without filters\" causes images to blur significantly. The images maintain rough features but loose details.  
The last two rows present results for networks trained with filtered labels/reference images. The \"Fully CNN\" network in this setup does not carry forward noises for the size of individual pixels, but by doing so, it also loses some of the minor features. It is not hard to differentiate it from the original image, but the image itself is still useful for any kind  
Figure 7: Examples of outputs generated by four neural networks configuration juxtaposed with input images. Outputs of the neural networks learned using filtered labels are slightly less sharp. All the presented networks can compress the data in the images and decode it very well. (**a**) Original Inputs. (**b**) Fully CNN—Without filters. (**c**) Compressing CNN—Without filters. (**d**) Fully CNN—With filters. (**e**) Compressing CNN—With filters.  
of processing. \"Compressing CNN\" trained with filters blurs the image more than \"Fully CNN.\" The image maintains visibly more details than the same network without filtering (row b). This suggests that the neural network with labelled outputs learns better feature sets and thus can replicate the original image more accurately, even though it was trained to match slightly blurred images.