Crater detection via genetic search methods to reduce image features

Cohen, Joseph; Ding, Wei

doi:10.1016/j.asr.2013.05.010

Cited by 23 publications

(4 citation statements)

References 22 publications

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“…The sum of the pixel values in one rectangle are subtracted from the sum of the pixel values in the other. Haar features are discriminative in face and crater detection [Cohen and Ding, 2013] because these domains have similar contrast at specific positions of the candidates. In this work Algorithm 1: Rotate Building Candidate By aligning buildings and adding padding to expose its edges, which have high contrast, we are able to obtain contrast patterns between candidates.…”

Section: Building Candidate Feature Constructionmentioning

confidence: 99%

Rapid building detection using machine learning

et al. 2016

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Abstract. This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available. We utilize new candidate search and feature extraction techniques to reduce the problem to a machine learning (ML) classification task. Here we can harness the complex patterns of contrast features contained in training data to establish a model of buildings. We avoid costly sliding windows to generate candidates; instead we innovatively stitch together well known image processing techniques to produce candidates for building detection that cover 80-85% of buildings. Reducing the number of possible candidates is important due to the scale of the problem. Each candidate is subjected to classification which, although linear, costs time and prohibits large scale evaluation. We propose a candidate alignment algorithm to boost classification performance to 80-90% precision with a linear time algorithm and show it has negligible cost. Also, we propose a new concept called a Permutable Haar Mesh (PHM) which we use to form and traverse a search space to recover candidate buildings which were lost in the initial preprocessing phase. All code and datasets from this paper are made available online 3 .

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Section: Building Candidate Feature Constructionmentioning

confidence: 99%

Rapid building detection using machine learning

et al. 2016

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“…For the classification of meteorite craters, Cohen [18] proposed a meteorite craters detection and classification algorithm based on a genetic algorithm. Jin [19] proposed a neural network method to automatically detect and classify meteorite craters.…”

Section: Introductionmentioning

confidence: 99%

Moon Impact Crater Detection Using Nested Attention Mechanism Based UNet++

2021

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Impact craters are the most prominent topographic feature on the lunar surface, which will play a significant role in constructing lunar bases and lunar surface activities in the future. Traditional meteorite crater recognition methods are mainly based on artificial interpretation, usually combined with classical image processing methods. However, due to the different diameters and shapes of impact craters, the traditional crater identification methods have significant errors and low efficiency for small or overlapping impact craters. This paper proposes an automated algorithm termed nested attention-aware U-Net (NAU-Net) for crater detection using a lunar's digital elevation model (DEM). It then uses template matching to effectively calculate the longitude, latitude, and radius of the crater. It is worth mentioning that NAU-Net is primarily based on UNet++ and attention networks and applies a sequence of nested dense convolution blocks, preferably of classical convolution, which combines U-Net++ and Attention Gates advantages. Since our network uses nested intensive attentional aware connections and in-depth supervision, the training process of the NAU-Net is simple, which can achieve more accurate detection and recognition. In fact, the network can recognize smaller or overlapping impact craters and larger and more complex ones. In honor of lunar impact craters, compared with U-Net, UNet++, Dense-Unet, Attention-Unet, R2-Unet, our model achieved recall rates and accuracy of 0.791 and 0.856, better than other improved U-Net models. The experimental results show that the NAU-Net model can be used to extract impact craters.

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“…However, due to the low resolution of early DEM data, the small crater recognition was very bad. For the remote sensing image, many scholars use edge detection [11], genetic search algorithms [12] to extract crater images. Then using hough transform or least squares to fit and match the crater.…”

Section: Introductionmentioning

confidence: 99%

The crater with gravity anomaly in the center may be the ancient volcanic crater and geothermal under it

Cui

et al. 2019

Therm sci

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Lunar volcanism play an important role on studying the thermal and compositional evolution of the Moon. However, the studies on the relationships among composition, location and age of Lunar volcanos are still limit. The high-quality and multi-source remote sensing data offer the opportunity to obtain significant features of the Lunar volcanism and the evolution of the Moon. Specifically, the high-quality gravitational features of volcanic landforms of the Moon are observed by the Gravity Recovery and Interior Laboratory. Besides, the lunar reconnaissance orbiter camera provides detail morphologic features of Lunar volcanos based on high-resolution optical images. This paper aims to find the characteristic of lunar volcanos by observing the gravitational and morphologic features in the center of craters. The final results show that most of the craters with central peaks have gravity anomalies except the Mendeleev crater (5.7 °N and 140.9 °E) whose central area contains significant gravity anomalies but no central peaks. The area of gravity anomaly may indicate heat source under the ground.

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Crater detection via genetic search methods to reduce image features

Cited by 23 publications

References 22 publications

Rapid building detection using machine learning

Rapid building detection using machine learning

Moon Impact Crater Detection Using Nested Attention Mechanism Based UNet++

The crater with gravity anomaly in the center may be the ancient volcanic crater and geothermal under it

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