It is of great significance to apply the object detection methods to automatically detect boulders from planetary images and analyze their distribution. This contributes to the selection of candidate landing sites and the understanding of the geological processes. This paper improves the state-of-the-art object detection method of YOLOv5 with attention mechanism and designs a pyramid based approach to detect boulders from planetary images. A new feature fusion layer has been designed to capture more shallow features of the small boulders. The attention modules implemented by combining the convolutional block attention module (CBAM) and efficient channel attention network (ECA-Net) are also added into YOLOv5 to highlight the information that contribute to boulder detection. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset which is widely used for object detection evaluations and the boulder dataset that we constructed from the images of Bennu asteroid, the evaluation results have shown that the improvements have increased the performance of YOLOv5 by 3.4% in precision. With the improved YOLOv5 detection method, the pyramid based approach extracts several layers of images with different resolutions from the large planetary images and detects boulders of different scales from different layers. We have also applied the proposed approach to detect the boulders on Bennu asteroid. The distribution of the boulders on Bennu asteroid has been analyzed and presented.

The digital orthophoto map of planetary bodies plays a significant role in landing site selection, traverse path planning, and scientific research. However, existing orthorectification methods in the planetary mapping community exhibit low-computational efficiency for linear pushbroom images. To solve this problem, this letter presents a novel orthorectification method based on an improved back-projection algorithm. The back-projection algorithm is based on the geometric constraints of the central perspective plane (CPP) and is further improved to process linear pushbroom images with distortions. Specifically, we segment the linear array in the focal plane into multiple line segments and find the exact CPP using simple analytical geometric calculations. The proposed method was fully tested and evaluated with well-known planetary mapping software packages, namely, the integrated system for imagers and spectrometers (ISIS). The experimental results demonstrated that compared with ISIS, the proposed orthorectification method can increase the computational efficiency by more than fivefold and deliver consistent geometric accuracy. The proposed orthorectification method greatly enhances geometric processing capabilities for massive planetary remote sensing images.

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km². The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m.

Linear array cameras are widely used in the fields of aerial and space remote sensing. The ground‐to‐image transformation plays a significant role in the geometric processing procedures of linear pushbroom images, which is time‐consuming due to multiple iterative calculations. Moreover, the detectors of the linear array may exhibit a curved shape due to distortions, resulting in a more complex ground‐to‐image transformation. This paper presents a novel ground‐to‐image transformation algorithm for linear pushbroom images based on a rigorous sensor model. We regard the distance between the back‐projected image point and the curved shape linear array as the “generalized distance.” The idea of generalized distance prediction was developed to efficiently determine the best scan line corresponding to the ground point. The plane analytic geometry is employed to solve the practical problems of the ground‐to‐image transformation, including the iterative direction, convergence condition, and subpixel interpolation. The applications of the proposed method in orthorectification and image matching were investigated. The proposed method was comprehensively tested using airborne images, Earth observation satellite images, and planetary images. The experimental results demonstrate that the proposed method (1) delivers high accuracy (better than1 × 10⁻⁴pixels) and efficiency, (2) is better than the cutting‐edge method based on the geometric constraints of central perspective plane for the linear pushbroom images with distortions, and (3) provides promising applications for generating orthophotos and image matching. The proposed method is easy to implement and efficient, which can greatly enhance the geometric processing abilities for various types of linear pushbroom images.

Different imaging instruments are designed in the planetary exploration missions, which require respective photogrammetric software modules to support the geometric processing of planetary remote sensing images. To decrease the cost of software development and maintenance, this paper presents a generic pushbroom sensor model for planetary photogrammetry. Thus, various coordinate transformations can be conducted in a unified and efficient way, without considering the specific camera and the optical distortion equations. In terms of implementation, the camera file and orientation data file designed for the airborne linear array camera ADS40 are used to manage the interior orientation and exterior orientation parameters of planetary images. The generic pushbroom sensor model supports the summing mode, varying exposure times within an image and image distortions, which are typical problems in need of a solution in planetary mapping. Furthermore, an alternative photogrammetric process based on extracting tie points on approximate orthophotos is developed. The geometric accuracy and computational efficiency of the generic pushbroom sensor model were compared with the popular planetary cartographic software—Integrated System for Imagers and Spectrometers (ISIS). The experimental results demonstrate that the proposed generic pushbroom sensor model can (1) deliver the same geometric accuracy as the ISIS pushbroom sensor model, (2) greatly improve the computational efficiency of orthophotos generation and tie points extraction, and (3) support various types of planetary remote sensing images. Moreover, the proposed generic pushbroom sensor model reduces the cost of software development because different types of planetary images share the same code base.

Mars topographic data, such as digital orthophoto maps (DOMs) and digital elevation models (DEMs) are essential to planetary science and exploration missions. The main objective of our study is to generate a higher resolution DEM using the Mars Express (MEX) High Resolution Stereo Camera (HRSC). This paper presents a novel pixel-level image matching method for HRSC linear pushbroom imagery. We suggest that image matching firstly be carried out on the approximate orthophotos. Then, the matched points are converted to the original images for forward intersection. The proposed method adopts some practical strategies such as hierarchical image matching and normalized cross-correlation (NCC). The characteristic strategies are: (1) the generation of a DEM and a DOM at each pyramid level; (2) the use of the generated DEM at the current pyramid level as reference data to generate approximate orthophotos at the next pyramid level; and (3) the use of the ground point coordinates of orthophotos to estimate the approximate positions of conjugate points. Hence, the refined DEM is used in the image rectification process, and pixel coordinate displacements of conjugate points on the approximate orthophotos will become smaller and smaller. Four experimental datasets acquired by the HRSC were used to verify the proposed method. The generated DEM was compared with the HRSC Level-4 DEM product. Experimental results demonstrate that an accurate and precise Mars DEM can be generated with the proposed method. The approximate positions of the conjugate points can be estimated with an accuracy of three pixels at the original image resolution level. Though slight systematic errors of about two pixels were observed, the generated DEM results show good consistency with the HRSC Level-4 DEM.