# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 1 Introduction  
The rapid development of Artificial Intelligence (AI) and Convolutional Neural Networks (DNN) [1] in particular led to the creation of multiple immense databases for supervised learning. Most popular datasets, such as ImageNet [2], MS COCO [3], CIFAR [4], focus on optical images in color or grey-scale. Other popular types of images with numerous datasets are medical images (e.g., COVID-19 V7 database [5]) or Infrared Imagery (e.g., FLIR [6]). With open datasets also comes a variety of pre-trained neural networks (YOLO series [7], Resnet50 [8, 9], R-CNN [10]). Big datasets can also be used to extract sets of features that enable further implementation as part of DNN or statistical algorithms for image analysis. Recently Synthetic Aperture Radar (SAR) systems have gained increasing popularity. These devices enable the monitoring of large areas of the earth's surface from satellites [11, 12, 13], airplanes or UAVs [14]. One of the main advantage of SAR technology is its high resistance to environmental conditions. However, due to entirely different work principles than optical devices, these images carry immense amounts of hard-to-extract information. Proper processing of the images created by SAR can generate unique information on the terrain. Interferometry [12] facilitated the monitoring of ground activity (e.g., volcano area seismic changes [15]), while Coherent Change Detection algorithm helps in the detection of sub-wavelength object deformations [16] (e.g., car wheel traces), and histogram-based algorithms can provide a tool for image semantic segmentation [17]. Among AI solutions, a recent article investigated Deep Neural Networks for crop classification [11, 18, 19]. Despite the common usage of SARs, due to their governmental or military usage rather than civilian or commercial, few datasets exist for AI training (e.g., SpaceNet6 [20], SSDD [21], MSTAR [22]). An additional reason for their sparsity might be related to the complexity of the SAR classification/segmentation task for humans, which makes it almostimpossible to create a community-driven labeled dataset. Furthermore, no open-source image features are available for SAR imagery. This means that every classification/detection/segmentation/image stitching task requires the creation of a new neural network or finding a new set of features (Bag Of Words). This article presents various unsupervised and self-supervised learning techniques for DNN pretraining in feature detection for transfer learning or further analytical processing. The article's contribution can be divided into the following aspects:  
* A novel filter-based labeling algorithm for loss calculation for CNN Pretraining;
* An open-access Bag Of Words for SAR imagery and a set of complete Neural Networks. The first goal was to provide a novel approach for better autoencoder pretraining, which can be later used for transfer learning. As a result, neural networks trained using filtered images for loss function calculation perform better. Lower loss values were obtained for validation with and without filtering the validation label images. The second goal was to provide faster development of Computer Vision algorithms for researchers and engineers. The performed experiments consist of training a set of Convolutional Neural Networks with different depths for different applications. The article starts with the definition of a set of lossless data augmentation methods for dataset enhancement. Then, the description of supervised, self-supervised, and unsupervised learning methods concerning the SAR imagery is presented. Section 2.6 presents the novel filtering algorithm for autoencoders training. Then, dataset and loss values are described, and the results are presented. The results contain the loss metrics values achieved by the presented neural networks, features extracted, convolutional layers kernels, and the juxtaposition of input-output imagery for four networks.