Segmentation of Crescent Sand Dunes in High Resolution Satellite Images using a Support Vector Machine for Allometry

Azzaoui, M. A.; Masmoudi, Lhoussaine; Belrhiti, H. El; Chaouki, I. E.

doi:10.14569/ijacsa.2019.0101126

Cited by 5 publications

(1 citation statement)

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“…Therefore, the proposed approach consisted of investigating the use of computer vision algorithms to automate barchans dunes detection and segmentation. Barchan dunes had a characteristic crescent shape, which could be segmented using classical computer vision algorithms [4]. However, in the field, thousands of barchans moved simultaneously, with smaller dunes moving faster than larger ones, resulting into dune collisions.…”

Section: B Problem Statementmentioning

confidence: 99%

Barchan Sand Dunes Collisions Detection in High Resolution Satellite Images based on Image Clustering and Transfer Learning

Azzaoui¹,

Masmoudi²,

Belrhiti³

et al. 2020

IJACSA

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Desertification is a core concern for populations living in arid and semi-arid areas. Specifically, barchans dunes which are the fastest moving sand dunes put constant pressure over human settlements and infrastructure. Remote sensing was used to analyze sand dunes around Tarfaya city located in the south of Morocco in the Sahara Desert. In this area, dunes form long corridors made of thousands crescent shaped dunes moving simultaneously, thus, making data gathering in the field very difficult. A computer vision approach based on machine learning was proposed to automate the detection of barchans sand dunes and monitor their complex interactions. An IKONOS high resolution satellite image was used with the combination of a clustering algorithm for image segmentation of the dunes corridor, and a Transfer Learning model which was trained to detect three classes of objects: Barchan dunes, bare fields and a new introduced class consisting of dunes collisions. Indeed, collisions were very difficult to model using classical digital image processing methods due to the large variability of their shapes. The model was trained on 1000 image patches which were annotated then augmented to generate a larger dataset. The obtained detection results showed an accuracy of 84,01%. The interest of this research was to provide with a relatively affordable approach for tracking sand dunes locations in order to better understand their dynamics.

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Section: B Problem Statementmentioning

confidence: 99%