An Enhanced Unsupervised Feature Learning Framework For Radar Sounder Signal Segmentation

Unsupervised semantic segmentation is the method of discovering meaningful semantic contents within the image domain without using any labelled information. The learned semantic contents are then decomposed into distinct semantic segments with known ontology. The core task of an unsupervised feature learning algorithm is to produce dense features for every pixel with rich semantic content to form distinct clusters with compact information for the downstream task. In this work, we extend the previously developed Self-Supervised Transformer with Energy-based Graph Optimization (STEGO) architecture by integrating a convolution-based Expansive Network in the decoder along with the spatial similarity loss function for radar sounder signal segmentation. Experimental results on the Multi-Channel Coherent Radar Depth Sounder (MCoRDS) data confirm the capability of the proposed unsupervised segmentation method.