# RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets
## 5 Experimental Results
### Classification  
For classification, the single object dataset was acquired at 2 different ranges, 3.8 m and 6.3 m, with 360\({}^{\circ}\) rotations as shown in Table 1 and Figure 3. All the collected images were labelled with the correct object identity, irrespective of viewing range, angle and receiver height. A fixed size bounding box of 400 \(\times\) 400 cells, which corresponds to 3 m\({}^{2}\), was cropped with the object in the middle. As stated above, the original images were resized to the \(88\times 88\) input layer, and we used stochastic gradient descent (SGD) with the parameters shown in Table 4.  
To test the hypothesis that principled data augmentation by RADIO improves on the commonly used, image-inspired data augmentation techniques, we conducted a series of experiments, as shown in Table 5. The standard data augmentation (SDA) techniques for image data that we employed are random translations and image mirroring [22]. Random translations changes the object location over the image, reducing the contribution of the background during the learning process. Rotations are not used because we already have data at all angles in the image plane. Although RGB image augmentation may include colour changes--i.e., modifying the colour map--this has no equivalent in the radar data, which are monochromatic, and this type of augmentation is therefore not applied. To ensure comparability of results, we applied the same number of camera-based data augmentations to each sample, meaning that each dataset is of similar size, as shown in Table 5. From Table 1, we have 475 samples for training, and we decided to employ the data augmentation methods 41 times for each sample, resulting in a total of 19,475 artificially generated samples and 19,950 in total. In Table 5, range data augmentation includes both attenuation and change of resolution ranges between 1 and 12 m, the addition of speckle noise with \(\sigma\) [0.01 0.15], and four levels of background shift, as described in Section 3. To verify the use of RADIO augmentation at different ranges, and to avoid overfitting to range-specific features, we used the images from 3.8 m to train and the images from 6.3 m to test.  
\begin{table}
\begin{tabular}{l l} \hline \hline Learning rate (\(\alpha\)) & 0.001 \\ Momentum (\(\eta\)) & 0.9 \\ Epochs & 100 \\ Batch size & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Neural Network Parameters.  
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c} \hline \hline \multirow{2}{*}{Input} & Conv 16 & \multirow{2}{*}{MaxPool} & Conv 32 & \multirow{2}{*}{MaxPool} & Conv 64 & \multirow{2}{*}{MaxPool} & Conv 128 & Conv 6 & \multirow{2}{*}{Label} \\  & (5 × 5) & 2 ×2 & (6 × 6) & 2 ×2 & (6 × 6) & 2 ×2 & (5 × 5) & (3 × 3) & \multirow{2}{*}{Distribution} \\  & ReLU & ReLU & ReLU & ReLU & ReLU & ReLU & Softmax & \\ \hline \hline \end{tabular}
\end{table}
Table 3: A-ConvNet Architecture.  
We used precision (\(\frac{TP}{TP+TP}\)), recall (\(\frac{TP}{TP+FN}\)), F-1 score (\(2\times\frac{precision\times recall}{precision+recall}\)) and accuracy (\(\frac{TP+FN}{TP+TN+FP+FN}\)) as metrics, where \(T\) is true, \(F\) is false, \(P\) is positive and \(N\) is negative.  
The key observation from Table 5 is that the progressive augmentation of the training data by the RADIO methods leads to progressively improved scores according to all the commonly used metrics. The key comparison is between the second and fifth row of the table, in which equally sized training datasets were used, but the use of the radar adapted augmentation leads to an improvement from \(\approx\)80% to 99.79%. The only mistake is between the sign and the mannequin, as both have a very similar shape signature. Admittedly, this improvement is in a relatively easy scenario, as the object location is pre-defined in a classification window. In Section 5.2 we address a more challenging scenario in which multiple objects are seen in a laboratory setting with potential occlusion and mutual interference, and there is additional background clutter caused by unknown artefacts.