## 2 Related Works
### Deep-Learning-Based Semantic Segmentation  
A pixel of an image can be in a part of a certain region in that image with its own characteristics to form a region type, and an image is a combination of one or more region types. Semantic segmentation correctly labels each pixel according to its relevance to a certain region type. In segmented output, spatial information must be retained until the end result. A software-based radio is likely to be a portable device, hence it can be implemented in a mini-computer or an SBC. Its computing capabilities must be considered before designing a proper deep leaning model to be implemented in such a device. Deep convolutional neural networks (CNNs) which are successful at image classifications such as VGG16 [20] have continuing convolutional layers for feature extraction. The work presented in [21] proves the capability of end-to-end training methods for pixel-to-pixel semantic segmentation with fully convolutional networks (FCNs). These networks take an arbitrary input size and produce a same-sized segmented output. In FCNs, pretrained deep CNNs such as AlexNet [22], VGG [20], and GoogLeNet [23] have been used for segmentation. AlexNet and GoogLeNet are much easier to test with a low power portable computer which can be used as a software-based image receiver. Internal mathematical operations other than tensor calculations of a deep learning model decide the additional processing power and the amount of memory needed by it. Deep deconvolutional neural networks have eliminated the need of saving pooling indices [24; 25]. Auto-encoders have been an inspiration for many segmentation techniques [12; 26; 27]. An input is encoded to a feature space which can be decoded into spatial categorization to achieve segmentation. SegNet [25] consists of an encoder which was followed by a decoder network, and it has topological similarities with VGG16. A better candidate to be implemented in an SBC is LinkNet [12], and it has a mechanism to bypass spatial information directly from its corresponding encoders to decoder blocks. At each encoder, there is a possibility of information loss, but LinkNet can preserve a considerably large amount of information without losing details. ParseNet is an end-to-end, effective CNN for semantic segmentation, which uses a technique to add global context to full CNNs [28].  
[PERSON] et al. introduced U-net [11] for biomedical images semantic segmentation built upon FCNs [21]. Its internal concatenation operation can be performed in a system with low computational power. Its structure has a contracting path for context capturing and a symmetric expanding path for precise localization. Feature pyramid networks (FPNs), which are more tolerant of images with distortions because of their internal structure, showed significant improvement as a feature extractor and can be used for both object detection and semantic segmentation [29]. A robust segmentation method for noisy images was introduced by using an unsupervised denoising filter [30] for real-time images. A scalable subspace clustering method [31] was proposed, which includes a concise dictionary and robust subspace representation in a unified model.