# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 2 Materials and Methods
### Encoder Structure  
We use ResNet-101 [23] as the backbone network of the encoder structure. ResNet is a residual learning framework used to ease the training of networks that are substantially deeper than those used previously. The backbone network (ResNet)--which consists of conv1, a pool1, and four block modules--extracts high-level features from ice radar images (Figure 3a). Each block consists of several bottleneck units (Figure 3b). We use output stride to represent the ratio of input image spatial resolution to final output resolution, and set the output stride to 16. After the filtered image is input into the backbone network and passed through conv1 and pool1, the output resolution is 1/4 of the input image resolution. Then, after passing through block1, block2, block3, and block4 in turn, the output resolution becomes 1/8, 1/16, 1/16, and 1/16 of the input image, respectively. Here, the stride of the last pooling or convolutional layer in block3 is set to 1 to avoid signal decimation. Then, all subsequent convolutional layers are replaced with atrous convolutional layers with rate r = 2. Atrous convolution adaptively modifies the filter's field-of-view by changing the rate value, which allows us to extract dense feature responses without changing the image spatial resolution or learning any extra parameters [17; 18]. The backbone network finally outputs 2048 dimensional features as the input of the atrous spatial pyramid pooling module.  
Atrous spatial pyramid pooling (ASPP) is inspired by the efficacy of spatial pyramid pooling [24], which demonstrates that it is effective to resample features at different scales to accurately and efficiently classify regions of an arbitrary scale. We review the ASPP module proposed in [19], as shown in Figure 4a, where three atrous convolutions with different atrous rates, image-level features, and a 1*1 convolution are applied behind the network backbone. The features of all branch results are concatenated and then fed through a 1*1 convolution. Based on the success of the original ASPP module, we propose a new ASPP structure to further improve the classification performance and reduce training parameters, as illustrated in Figure 4b.  
Considering the ordered distribution of ice subsurface targets, the features captured by the atrous convolution with different rates in the ASPP module represent the global contextual information captured by image-level features. Therefore, we remove image-level features from consideration and use a four branches structure to replace the five branches structure of traditional ASPP. In addition, we use a 1*1 convolution with 512 dimensions to change the input feature dimension of atrous convolution. The original ASPP module is designed to evaluate data with respect to the PASCAL VOC 2012 semantic segmentation benchmark [25], which contains 20 foreground object classes and one background class, and has an input feature dimension of 2048. However, ice radar images can only be divided into four categories. In the original ASPP module, the input feature dimension of 2048 is high, which will not only cause feature redundancy, but also increase the training time. Therefore, a 1*1 convolution is employed to reduce both the input feature dimension (to 512) and the time needed to reconstruct  
Figure 4: (**a**) The original ASPP structure. (**b**) The improved ASPP structure. We changed the input feature dimension of the atrous convolution and removed the image-level features.  
Figure 3: (**a**) Resnet-101 as the backbone network. (**b**) Several bottleneck units reside within each block.  
features. Similarly, we modify the encoder output feature from 256 to 64, which is beneficial to further refine features used in the decoder.