Tensor-Decomposition-Based Unified Representation Learning From Distinct Data Sources For Crater Detection

The deep-learning-based detection of planetary craters provides significant assistance for in-orbit vehicles in space exploration. However, most object detection researches to date tended to focus on the single-source data collected by mono-type sensors, and their performances were severely hindered by the disability of using diverse source data from various sensors. For effectually using distinct sources in resource-limited vehicles, we present a compact framework to learn unified representation from elevation and visual sources basing on tensor decomposition. In particular, the vital elevation is extracted to obtain the deficient information in visuals. Furthermore, for accommodating limited computing resource, a compound tensor decomposition layer is proposed based on the special structure of decomposition, which extracts the source-specific and source-independent information, and at the same time crafts unified representation for terrains. Comprehensive comparisons with recent methods demonstrate the effective representation learning of the proposed method on different planetary terrains and in insufficient data learning setting, revealing its potential for real-world applications.