# Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map

## TL;DR

This paper introduces a new method for detecting small lunar impact craters using advanced image processing and machine learning techniques to improve accuracy and efficiency.

## Contribution

The novel PSEF method combines pseudo-spectral spatial features with YOLO for enhanced detection of meter-sized lunar craters.

## Key findings

- The PSEF model outperforms the Basal model in precision, recall, F1-score, mAP, and robustness.
- Statistical analysis confirms the PSEF model's excellence in predicting crater size, shape, and location.
- The method provides a more accurate and consistent way to detect meter-sized craters on planetary surfaces.

## Abstract

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system.