A hybrid wavelength selection strategy-based quantitative analysis model for LIBS data from standard ground samples of the Curiosity rover on Mars

Yu, Yang; Yao, Meibao; Jian-ping, Huang

doi:10.1039/d2ja00216g

Cited by 6 publications

(7 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, to obtain a high-edge accuracy in an extremely challenging task, pure complex structures are insufficient, and an understanding of the data based on spectral expertise is essential. The wavelength interval selection method considers each wavelength interval as a unit, which improves the predictive capability, provides easier interpretation, and enables reliable calibration, such as: Moving windows PLS (MWPLS) [36], interval PLS (iPLS) [13], and synergy iPLS (SiPLS) [6]. As illustrated in Figure 2, when the stride step is smaller than the CKW, the convolution kernel overlaps with the spectral signals during movement, thus, more features can be extracted.…”

Section: Cnn Modeling and Training Processmentioning

confidence: 99%

“…In this study, the major elements Si, Al, and K were analyzed because they cover three representative ranges, and their concentration differences are important characteristic indexes that reflect the variation of sedimentary conditions [4]. They are also widely chosen as examples for mineral identification, emission line selection, element abundance identification, and other LIBS tasks [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Modeling the quantitative relationship between target components and measured spectral information is an essential part of laser-induced breakdown spectroscopy (LIBS) analysis. However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.

show abstract

Section: Cnn Modeling and Training Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method classifies the samples first and then conducts quantitative analysis. Yan et al 14 used the method of model population analysis combined with variable selection to conduct quantitative analysis of ground standard samples of the ChemCam team.…”

Section: Introductionmentioning

confidence: 99%

Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning

Rao,

Ren,

Kong

et al. 2024

J. Anal. At. Spectrom.

View full text Add to dashboard Cite

show abstract

“…In addition, rocks can cause damage to the rover, for instance, the aluminum wheels of the Curiosity rover were severely damaged by the hard rocky surface during its mission. Therefore, it is very important to correctly identify the rocks in the scene for the path planning and target selection during the exploration of the Mars rover [3,4]. However, rocks vary in shape, texture, and other features, and parts of it may be covered with dust or embedded in soil [5], thus exacerbating the difficulty of the above task.…”

Section: Introductionmentioning

confidence: 99%

RSU-Net: An Attention U-Net for Martian Rock Segmentation

Tian,

Yao

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Since rocks may collide with the rover or wear tires during the exploration mission of the Mars probe, and may contain rich geological information, identifying rocks in the scene is crucial for the navigation and obstacle avoidance of the Mars probe. Additionally, since the communication between the Mars rover and the earth is often intermittent and delayed during missions, it needs a certain degree of autonomy. Deep learning technologies such as semantic segmentation and target detection can meet this requirement to a certain extent, which facilitates the enhancement of safety and efficiency for the Mars rover. Rock segmentation is to divide the pixels of the rock from the image. However, the texture of the rock is often close to the texture of the surrounding sand, and some parts may be covered, so it is difficult to identify it correctly. To this end, this paper proposed RSU-Net (Rock Segmentation U-Net) and RSU-Net-L, which combine the SENet attention mechanism, and the latter achieves higher computational efficiency and inference speed by compressing the number of channels on the basis of the former. In addition, this paper established a dataset, MarsRock, for Mars rock segmentation to help the Mars rover for visual navigation. Its images come from “Tianwen-1”, which contains 1194 images, and each image has a corresponding rock label. And our experiments on the MarsRock dataset show that RSU-Net can achieve 99.07% accuracy and 67.71% F1-score. RSU-Net-L can achieve 98.99% accuracy and 66.67% F1-score while diminishing the number of parameter count by 43.7% and the number of FLOPs by 43.6%, while the FPS can reach 12.01 on a single RTX6000-24GB GPU.

show abstract

A hybrid wavelength selection strategy-based quantitative analysis model for LIBS data from standard ground samples of the Curiosity rover on Mars

Abstract: Laser-induced breakdown spectroscopy (LIBS) is a useful tool for the in situ detection of material components on Mars. This technique can directly acquire key information, such as the types and...

Cited by 6 publications

References 66 publications

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Rapid quantitative analysis of raw rocks by LIBS coupled with feature-based transfer learning

RSU-Net: An Attention U-Net for Martian Rock Segmentation

Contact Info

Product

Resources

About