Vision‐based crater and rock detection using a cascade decision forest

Abstract

Both crater and rock detection are components of the autonomous landing and hazard avoidance technology (ALHAT) sensor suite, as craters and rocks represent the majority of landing hazards. Furthermore, places with scientific values are very probable next to craters and rocks. Unsupervised approaches, which potentially use the pattern recognition techniques of ring threshold finding, perform quickly; however, they suffer from handling small craters. The supervised pattern recognition method is more powerful but is time-consuming. To address these issues, here, a simultaneous multi-size crater and rock detection algorithm is studied. The authors propose a new supervised machine-learning framework using a cascade decision forest. Sliding windows are utilised in order to search basic features, and a multi-grained cascade structure is introduced to enhance the framework's ability to learn the representations of the features. The training time of the proposed algorithm on a PC is comparable to that of deep neural networks, and the efficiency is enhanced for a large-scale database. The outputs of the simulation verify the effectiveness and validity of the introduced technique.

A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, Using Machine Learning

“…Training of our model was performed on the Prism sub‐cluster of NASA's Advanced Data Analytics PlaTform (ADAPT) at the NASA Center for Climate Simulation (NCCS). The loss function to be minimized during training was a categorical cross entropy function, which is commonly used for multiclass classification problems (e.g., Yan et al., 2019) and provides an accurate measurement of the distance between the predicted distribution and the true class labels. We set a batch size of 64, a learning rate of 3 × 10 −4 , and the maximum number of epochs to 1,000.…”

Target Localization for Autonomous Landing Site Detection: A Review and Preliminary Result with Static Image Photogrammetry

“…Kang et al (2018) [22], Xin et al (2017) [23], and Urbach et al ( 2009) [24] introduced several Supervised Learning techniques for object classification and localization, which include Support Vector Machine, Ada Boost, and Notation of Looking for Perspective. The KLT detector discussed in [25] employs a combined approach of Supervised and Unsupervised Learning, while Template Matching, which is a combination of Supervised and Deep Learning approaches, was discussed in [26].…”

“…To detect craters, a combined detection methodology employs both unsupervised and supervised detection methods. For example, consider the KLT detector, which is a combination detection technique, to extract probable crater regions [25]. In this approach, supervised detection methodology was used, and image blocks were used as inputs, while the detection accuracy was significantly influenced by the KLT detector's parameters.…”

Robust Crater Detection Algorithm Based on Maximum Entropy Threshold Segmentation

“…Studies by Wang et al [19] presented a high generalization performance transfer learning-based CDA. Some traditional methods are also used in this field [20], such as the Hough transform, Canny edge detection [21,22], cascade decision forest [23], solar illumination direction, and texture analysis [24]. Jia et al extracted more all craters larger than 200 m of Chang'e-5 landing area based on digital orthophoto map generated from more than 700 Lunar Reconnaissance Orbiter Camera, Narrow Angle Camera [25].…”