EXPLORING THE RELATIONSHIPS BETWEEN SCATTERING PHYSICS AND AUTO-ENCODER LATENT-SPACE EMBEDDING

Polarimetric SAR (PolSAR) is uniquely able to capture structural and compositional properties of targets leading to improved performance in various classification applications over traditional single-polarization SAR. To aid in the interpretation of the return scatter, several decomposition techniques have been developed that attempt to classify the scene by presenting the return power as a combination of pre-determined canonical targets. For most decomposition techniques, these relationships are derived from the physics of radar scatter, and thus the results of the segmentation are explainable in terms of observable physical phenomena. Recently, Deep Neural Networks (DNNs) have emerged as a leading strategy for classifying PolSAR data. While effective, these techniques are dependent on discovering relationships between the data and provided supervised labels during an exploratory phase of the algorithm called “training”. The inter-dependencies are embedded into the weights of the network and are subsequently used to perform classification of the unlabeled data samples. Since the entire process is data-driven, it can be difficult to explain the outcomes of the algorithm physically. In this paper, we begin to explore the relationship between radar physics and the latent space embedded in the networks during supervised training. The goal is to explore current and future possibilities of explaining the results of deep neural networks and relating their outputs to radar physics. This can help improve confidence in the outputs of DNNs and potentially illuminate strategies to further improve their performance by embedding physical constraints in their training and classification.