1. Introduction
China’s first Mars exploration mission, Tianwen-1, was launched on 23 July 2020, with the aim of completing orbital, landing, and roving tasks. The high-resolution imaging camera (HiRIC) [
1] carried by the Tianwen-1 orbiter provided detailed images of the landing area. The landing area is dotted with numerous craters, which can offer crucial information about the evolution of the landing region. Craters, as one of the most prominent features on the planetary surface, play a key role in planetary science. The distribution of craters provides valuable clues for revealing the aging process of geological structures [
2,
3] and is also one of the important factors in selecting landing sites for planetary probes [
4]. Calculating the size–frequency distribution of craters is a primary method for estimating the surface age of celestial bodies [
5,
6], making it a key technique in planetary geological research. Therefore, the automatic segmentation and statistical analysis of the features of Martian surface craters are crucial in Martian exploration missions. Traditional methods [
2,
7] rely on planetary scientists manually labeling craters on remote sensing images, which is time-consuming and labor-intensive, and cannot detect craters in real time. Therefore, an efficient, accurate, and unsupervised method for automatic crater detection is highly necessary.
Currently, several methods for automatic crater detection have been developed, which can be roughly categorized into methods based on extraction of the morphological features and methods based on deep learning. Methods based on extraction of the morphological features utilize intuitive and simple features, such as the craters’ shape or spectral characteristics, such as the Canny edge contour algorithm [
8], the Hough transform [
9], and template matching [
10]. Yang et al. [
8] used a multi-scale Canny operator to detect the edges of craters. However, variations in the illumination conditions between different images may affect the performance of edge detection, resulting in suboptimal crater detection, Leroy et al. [
11] utilized template matching and probability volume analysis to identify Martian craters larger than 5 pixels (an image resolution of 250 m/pixel). However, the experimental scenarios were relatively simple, with mostly regular circular craters. Galloway et al. [
12] investigated the impact of preprocessing via Canny edge detection on the performance of the Hough transform, finding that edge detection significantly affected the performance of the Hough transform, and they considered the Hough transform to be a more practical method than edge detection. However, their approach requires computing gradients for each edge pixel, which is time-consuming, and they did not test the performance when detecting multiple-sized craters in larger images. These methods based on the extraction of morphological features can effectively detect simple craters in small datasets. However, due to the complexity of the terrain conditions and the common occurrence of densely distributed craters in real-world scenarios, these methods exhibit relatively lower detection rates and efficiency when dealing with large-scale datasets.
To improve the efficiency of automatic crater detection, deep learning has been widely applied in crater detection tasks. Convolutional Neural Network (CNN)-based deep learning algorithms have achieved tremendous success in segmentation and detection tasks in remote sensing images [
13,
14]. Li et al. [
15] introduced a novel encoder-decoder-based network, MarsSeg, specifically tailored for segmentation of the Martian landscape, and demonstrated its advanced performance. Silburt et al. [
16] successfully used the U-Net [
17,
18] model and template matching algorithm to extract craters on lunar DEM images. Since the U-Net network layers they used were relatively shallow, the segmentation ability for the craters’ outlines was poor. Lee et al. [
19] utilized the U-Net model trained on lunar DEM images by Silburt et al. to identify and extract craters in Martian DEM images, confirming the effectiveness of the U-Net model in crater detection. In order to enhance the efficiency of crater detection and the accuracy of identifying large Martian craters, Chen et al. [
20] proposed a novel CNN called MC-UNet. They incorporated average pooling and embedded channel attention into the skip-connection process between the encoder and decoder layers to identify craters in optical images of Mars. To improve the performance and accuracy of automatic crater identification, Zhao et al. [
21] explored a novel embedded U-Net structure based on U-Net, named Square U-Net (SqUNet). They used the embedded U-Net architecture to replace traditional convolutional modules, successfully achieving high-precision identification and extraction of lunar DEM craters, and demonstrating high-precision test results on Martian DEM as well. However, these algorithms for automatic crater detection are supervised models, requiring many labeled crater datasets for training. The currently available global catalogs of lunar craters [
2,
7,
22] include craters with diameters greater than 1 km, while the global catalog of Martian craters includes craters with diameters greater than 1 km [
23]. The Chang’e-5’s landing area has a catalog of craters with diameters larger than 200 m [
24]. However, most landing areas lack a catalog of meter-sized craters. Therefore, in the absence of a crater catalog, a model that does not require additional training would be highly valuable.
In the remote sensing field, several foundational large models tailored for segmentation tasks have emerged, such as RSPrompter [
25] and SAM [
26]. These foundational large models are typically trained on extremely large annotated datasets, endowing them with strong generalization capabilities for segmentation tasks. Therefore, they can be easily extended to other segmentation tasks. SAM [
26], in particular, is suitable for unsupervised segmentation tasks. SAM was initially designed to address a fundamental challenge in image segmentation: how to effectively identify and segment objects in images without training data that are specific to a particular task. SAM aims to create a universal model that is capable of adapting to different types and sizes of images, automatically identifying and segmenting target objects in these images, even if these objects have never been encountered by the model before. SAM networks have achieved significant success in various domains. In the field of medical imaging, SAM has been used for the automatic identification and segmentation of structures in various medical images [
27], which is particularly important in situations with a scarcity of annotated medical images. In processing remote sensing images, Giannakis et al. [
28] validated the effectiveness of SAM in identifying planetary craters by providing examples from different celestial bodies and types of data. However, extracting crater targets from high-resolution planetary images remains challenging due to SAM’s ability to extract any type of target from images.
The Tianwen-1 mission landed in the southern part of Utopia Planitia, located in the northern lowlands of Mars (25.1°N, 109.9°E). Tianwen-1 is a comprehensive mission aiming to study the morphology, mineralogy, spatial environment, and distribution of water ice on Mars [
29]. Many researchers have conducted detailed geological surveys of the Tianwen-1 landing area, gaining a deep understanding of the geological background and evolutionary history of Utopia Planitia [
30,
31,
32,
33]. However, studies on impact craters in the Tianwen-1 landing area have only extracted partial areas or a small number of large craters for an analysis of their distribution, lacking a detailed catalog of craters in the landing area. Currently, the Tianwen-1 landing area has a catalog of craters with diameters larger than 80 m [
34]. Cao et al. [
35] annotated over 7000 craters larger than 1 m in three subregions of the Tianwen-1 landing area, providing labels for small-sized craters. Providing a complete catalog of meter-sized impact craters for the landing area is crucial. Understanding the distribution, quantity, size, and other important parameters of craters not only helps deepen our understanding of the geological environment near the Mars landing site but also provides important references for future scientific research and exploration missions. This study aimed to extract multi-scale craters in the landing area and make a more detailed and in-depth analysis of the age and geological evolution of the landing area.
In this work, we used SAM to identify and extract craters from the digital image model (DIM) of the Tianwen-1 landing area. The SAM was used to identify craters in the DIM and obtain their segmentation mask, and a series of postprocessing steps was performed to extract the positions and sizes of craters. Through analyzing the CSFD of the craters, we estimated their specific surface age to be approximately ~3.25 Ga, with subsequent surface resurfacing events occurring around ~1.67 Ga. The main contributions of this article are as follows:
- (a)
We proposed a complete solution for automatic crater identification based on SAM, which was applied to extract craters from the DIM of the Tianwen-1 landing area.
- (b)
Experiments were conducted in three subregions of the Tianwen-1 landing area, where SAM achieved a recall rate of 100%. Many new craters were detected, confirming its effectiveness in the task of crater extraction.
- (c)
On the basis of the DIM of the Tianwen-1 landing area, we provided a relatively comprehensive crater dataset, including information on the position and diameter of the craters.
- (d)
We analyzed the CSFD of the craters and estimated the surface age of the landing area, conducting an analysis of the geological evolution of the landing region.
The structure of this article is as follows:
Section 2 introduces the complete process of using SAM to extract craters from the DIM of the Tianwen-1 landing area.
Section 3 describes the experimental results of SAM on the lunar crater dataset.
Section 4 presents the results of crater extraction, including an analysis of the number of craters extracted, an analysis of the craters’ size and distribution frequency, and an explanation of the catalog of craters.
Section 5 discusses the limitations of the study and outlines future work. Finally,
Section 6 draws some conclusions.
6. Conclusions
This article presents a comprehensive solution for automatic crater recognition based on the Segment Anything Model (SAM), aiming to extract craters in the DIM of the landing area of the Tianwen-1 mission. We utilized SAM to obtain segmentation masks for the craters and used a series of postprocessing steps, including non-circular filtering, circular fitting, deduplication, and removal of false positives, to extract the positions and sizes of craters. Experimental validation demonstrated the effectiveness of SAM in planetary crater identification tasks. In the DIM of the Tianwen-1 landing area, we extracted a total of 841,727 craters with diameters ranging from 1.57 m to 7910.47 m, providing a comprehensive crater dataset that contributes valuable data for future missions of detecting Martian crater detection. The distribution of the number of craters reflected the geological structure and characteristics of historical impact events in the Tianwen-1 landing area. Additionally, through the craters’ size–frequency distribution, we estimated the specific surface age of the landing area to be ~3.25 billion years, with subsequent surface resurfacing events occurring ~1.67 billion years ago.
In future work, we plan to further optimize and improve the crater extraction solution using SAM. This involves optimizing postprocessing algorithms for crater extraction by considering the diversity of craters. In addition, we also plan to explore the use of multi-source data fusion techniques for crater extraction, aiming to obtain more comprehensive and accurate information on craters. Multi-source data fusion techniques can integrate data from different sensors and observation platforms, providing more comprehensive information on craters’ feature. This not only enhances the accuracy and reliability of our existing models but also offers richer data support for scientific research. Furthermore, we plan to expand and improve the existing crater dataset, which will be crucial for future Martian exploration and research. Finally, we hope to apply these techniques to the study of craters on other celestial bodies such as the Moon, asteroids, etc., to further extend the reach of our research and provide more data and insights for the geological study of these celestial bodies. Through these efforts, we aim to offer more comprehensive and in-depth support for the geological exploration and research of Mars and other celestial bodies.