Searching for the sources of ice block falls at the martian north polar scarps

Su, Shao Bo; Fanara, Lida; Zhang, X.; Hauber, Ernst; Gwinner, K.; Oberst, J.

doi:10.1016/j.icarus.2022.115321

Cited by 5 publications

(23 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The training data account for 80%, the validation data account for 10% and the test data account for the rest 10%. The preprocessing of the HiRISE images including ortho-rectification and coregistration has been described in [11]. The tile width and length are set to 256 pixels, typically 64 m, because the vast majority of ice-fragments (the longer length < 100 pixels) are smaller than that.…”

Section: A Dataset and Implementationmentioning

confidence: 99%

“…However, it only took ∼8 min by using a NVIDIA GeForce GTX 1070 with Max-Q Design to process, orders of magnitude faster than manual work. Su et al [11] used an automated change detection method to detect the corresponding shadows of the detached ice-fragments in the same scarp region with the same images. The change detection method proposed by Su et al [11] can achieve an average true positive rate of ∼97.6% in detecting the shadows of the detached ice-fragments.…”

Section: G Application and Association Analysismentioning

confidence: 99%

“…Su et al [11] used an automated change detection method to detect the corresponding shadows of the detached ice-fragments in the same scarp region with the same images. The change detection method proposed by Su et al [11] can achieve an average true positive rate of ∼97.6% in detecting the shadows of the detached ice-fragments. As the detected ice-fragments in this study and the detected shadows in the article by Su et al [11] are relevant, we use the results of shadow detection to evaluate the detection of the detached ice-fragments.…”

Section: G Application and Association Analysismentioning

confidence: 99%

“…The change detection method proposed by Su et al [11] can achieve an average true positive rate of ∼97.6% in detecting the shadows of the detached ice-fragments. As the detected ice-fragments in this study and the detected shadows in the article by Su et al [11] are relevant, we use the results of shadow detection to evaluate the detection of the detached ice-fragments. To reduce interference from noise and radiance difference of bitemporal images, both detections of the detached ice-fragments and the shadows, smaller than 5 pixels, were excluded.…”

Section: G Application and Association Analysismentioning

confidence: 99%

“…There are two ways to estimate the area of the mass wasting volume: the vacant gap left in the source scarp and the collection of ice blocks at the foot of the scarp. The source regions of block fall events were first mapped with the help of changed shadows' detection by [10], [11]. Fig.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Detection of Detached Ice-fragments at Martian Polar Scarps Using a Convolutional Neural Network

Fanara

Xiao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Repeated high-resolution imaging has revealed current mass wasting in the form of ice block falls at steep scarps of Mars. However, both the accuracy and efficiency of ice-fragments' detection are limited when using conventional computer vision methods. Existing deep learning methods suffer from the problem of shadow interference and indistinguishability between classes. To address these issues, we proposed a deep learning-driven change detection model that focuses on regions of interest. A convolutional neural network simultaneously analyzed bitemporal images, i.e., pre-and postdetach images. An augmented attention module was integrated in order to suppress irrelevant regions such as shadows while highlighting the detached ice-fragments. A combination of dice loss and focal loss was introduced to deal with the issue of imbalanced classes and hard, misclassified samples. Our method showed a true positive rate of 84.2% and a false discovery rate of 16.9%. Regarding the shape of the detections, the pixel-based evaluation showed a balanced accuracy of 85% and an F1 score of 73.2% for the detached ice-fragments. This last score reflected the difficulty in delineating the exact boundaries of some events both by a human and the machine. Compared with five state-of-the-art change detection methods, our method can achieve a higher F1 score and surpass other methods in excluding the interference of the changed shadows. Assessing the detections of the detached ice-fragments with the help of previously detected corresponding shadow changes demonstrated the capability and robustness of our proposed model. Furthermore, the good performance and quick processing speed of our developed model allow us to efficiently study large-scale areas, which is an important step in estimating the ongoing mass wasting and studying the evolution of the martian polar scarps.

show abstract

Section: A Dataset and Implementationmentioning

confidence: 99%

Section: G Application and Association Analysismentioning

confidence: 99%