Automatic detection of sub-km craters in high resolution planetary images

Urbach, Erik R.; Stepinski, T. F.

doi:10.1016/j.pss.2009.03.009

Cited by 118 publications

(74 citation statements)

References 19 publications

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“…By computing the mapping between parameter space and image space, the algorithm can find the target objects within a certain class of shapes. The work most related to our framework is the crater-detection method proposed by Urbach and Stepinski in [2]. Similarly in [2], we identify a crater by two components: a bright region and a shadow region.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly in [2], we identify a crater by two components: a bright region and a shadow region. Based on the preliminary work done in [2], we perform efficient KDD process [3] on feature creation and feature selection, training set sampling, and utilizing multiple classification methods to improve the detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Instead of using edge detection for circular crater rims, we observe that a crater can be recognized as a pair of highlight and dark crescent regions. First, image processing techniques, described in our early work in [2], are applied to remote sensing images, including removing irrelevant background, identifying crescent shapes, constructing crater candidates by combining highlight and dark regions. Second, we design and implement an automated crater-detection framework using knowledge discovery and data mining (KDD) process [3].…”

mentioning

confidence: 99%

“…Then we use a greedy algorithm to search for the best features and the best classifiers. For the purpose of comparative study, we evaluate the performance of our framework at the same site centered on the Nanedi Valles on Mars used in [2]. The detection rate of F1 measure has been improved significantly from 0.613 to 0.772, where 0.613 is the reported detection rate in [2].…”

mentioning

confidence: 99%

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Effective classification for crater detection: A case study on Mars

Wang

Ding

Fradkin

et al. 2010

9th IEEE International Conference on Cognitive Informatics (ICCI'10)

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Abstract-Craters are important geographical features caused by the impacts of other celestial bodies. Craters have been widely studied because they contain crucial information about the age and geologic formations of a remote planet. This paper discusses an automated crater-detection framework using knowledge discovery and data mining (KDD process) including sampling, feature selection and creation, and supervised learning methods. The framework is evaluated on a real world case study on Mars crater detection. Compared with the existing method, the detection rate of F-measure is improved from 0.613 to 0.772 on a studied Martial site of area 451,562,500 m 2 . Session-Data Mining

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Effective classification for crater detection: A case study on Mars

Wang

Ding

Fradkin

et al. 2010

9th IEEE International Conference on Cognitive Informatics (ICCI'10)

Self Cite

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“…A state-of-art framework for crater recognition was developed including crater candidates selection and craters classification by supervised machine learning [2]. Automatic detection of craters is difficult when their rims are unclear, segmented, or the image is noisy (degradation, internal morphologies).…”

Section: Introductionmentioning

confidence: 99%

Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition

Liu

Chen

Zhou

2015

IEICE Trans. Inf. & Syst.

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SUMMARYRobust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

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Evaluation of machine learning algorithms to classify and map landforms in Antarctica

Siqueira

Veloso

Filho

et al. 2021

Earth Surf Processes Landf

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The detailed geomorphology of ice‐free landscapes of Antarctica is key to understanding how their highly fragile environments respond to climate change, at different temporal and spatial scales. Despite the recent advances in geomorphological studies of ice‐free areas, machine learning applications to produce landform maps are still scarce on the Antarctic continent. In this study, we evaluated the predictive performance of different supervised machine learning algorithms to produce digital geomorphological maps in Vega Island—Antarctic Peninsula region. We tested six different models: average artificial neural networks, C5.0 decision tree, random forest, support vector machine, supervised self‐organizing map and weighted k‐nearest neighbours. We used an initial set of 54 geomorphometric and spectral predictors, from which redundant variables with Pearson correlation coefficient >|0.95| were removed, and only the most important predictors for each model were selected using recursive feature elimination. For training, we ran each model 100 times and predictions were assessed by the kappa and global accuracy values. The best predictors were the Red Edge 6 and SWIR 11 bands, roughness concentration index, elevation and drainage density. The decision trees C5.0 and random forest had the best performance, with average validation kappa of 0.85 ± 0.03 and 0.84 ± 0.03, respectively, evidencing excellent prediction. Despite the similar performance, random forest showed greater uncertainty degree and accuracy when classifying complex landforms, attesting to its great robustness. From sensitivity and specificity values, we observed that the glaciers and talus showed higher accuracy, whereas cryoplanated platforms and scree slopes had the worst classification. The presented methodology optimized the classification by selecting the most important predictors, assessing accuracy and evaluating uncertainty. The results indicated that machine learning methods have great potential to produce geomorphological mappings in the Antarctic ice‐free areas, as a promising tool to provide detailed information on remote, harsh polar environments.

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Automatic detection of sub-km craters in high resolution planetary images

Cited by 118 publications

References 19 publications

Effective classification for crater detection: A case study on Mars

Effective classification for crater detection: A case study on Mars

Discriminative Dictionary Learning with Low-Rank Error Model for Robust Crater Recognition

Evaluation of machine learning algorithms to classify and map landforms in Antarctica

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