A Multiscale Approach for Space Debris Detection Utilizing Morphological Features

Optical image-based space debris detection is crucial for preventing collisions between spacecraft in orbit. However, identifying debris amidst low signal-to-noise ratios (SNR) with crowded stellar environment remains challenging. To enhance faint debris detection within complex backgrounds, this study proposes a multi-scale approach that leverages the morphological feature differences between tracked space debris and non-tracked stars for their identification. The proposed adaptive multi-scale morphological transformation demonstrates robustness against adjacent star and noise interference, achieving superior local background estimation for debris segmentation. Furthermore, we construct a feature representation based on the differential response of point-like and streak-like objects to morphological operations. This representation exhibits significant discriminative power for objects of differing morphology, facilitating their recognition. The proposed method offers a streamlined, one-step process for acquiring space debris, in contrast to the traditional methods of segmentation followed by classification. Numerical experiments confirm that the method surpasses traditional techniques, achieving higher detection rates and fewer false positives, particularly for debris within dense stellar backgrounds.