Efficient semantic segmentation of radar sounder data

Radar sounders are active sensors that operate by transmitting electromagnetic waves toward the subsurface of a target area and capturing the reflected signals. The reflected signals are then processed to create images or maps of the target subsurface. Supervised deep learning techniques have emerged as powerful tools for radar sounder data analysis. However, they require a significant amount of labeled data, which is challenging given both the difficulty of acquiring such data in specific subsurface environments and the cost of labeling them with complex photointerpretation procedures. Recently, some methods have been proposed to segment the radargrams by employing deep complex models or pretrained networks. However, these methods may lead to models that are too complex for the problem and computationally inefficient. Thus, we present a computationally efficient u2net model that combines u2net and octave convolution. This combination offers the advantage of a deep efficient architecture with rich multi-scale features while keeps computational and memory requirements relatively low. The results show the efficiency and adaptability of our model to the availability of limited labeled data and its generalization capabilities when augmented with additional data. Furthermore, our proposed model significantly reduces the number of parameters used compared to existing methods for radar sounder segmentation.