Satellite Component Detection Based Upon Searchable Multiview Features Mapping

Satellite component detection (SCD) is one of the key technologies for on-orbit service (OOS) in space exploration. Unlike detection tasks on the ground, the single illumination in the space environment often leads to significant shadow occlusion when capturing satellite components from multiple viewing angles, resulting in the loss of component information. Existing component detection methods utilize this information to estimate their high-dimensional feature distributions and lack descriptions of the correlations between multiview features. To address this issue, we proposed an SCD based upon searchable multiview features mapping, which describes the distribution of satellite components by capturing both the discrimination between positive and negative samples and the commonality of intraclass components. Specifically, to enhance the connection of multiview features, orthogonal subspaces for different classes are constructed by the improved cosine distance between multiview features which is performed as a subspace metric to represent the discrimination and is used to cluster the mapped features. Besides, considering inherent errors in the mapping process, a loose constraint of the metric is introduced to search for common features, maintaining the local sparsity of multiview features. In the proposed dataset and the public dataset of satellite components, the proposed method achieved mean average precisions (mAP) of 0.878 and 0.862, respectively, outperforming existing state-of-the-art methods.