The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-4/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W12, 17–24, 2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W12, 17–24, 2019

  21 Feb 2019

21 Feb 2019


M. A. Azzaoui1, M. Adnani1, H. El Belrhiti2, I. E. Chaouki3, and L. Masmoudi1 M. A. Azzaoui et al.
  • 1Laboratoire d’Electronique et de Traitement du Signal/Géomatique (LETS/Géomat), Faculté des Sciences de Rabat, Université Mohammed V-Agdal, 4 Avenue Ibn Battouta B.P. 1014 RP, Rabat, Maroc
  • 2Département des Sciences Fondamentales et Appliquées. Institut Agronomique et Vétérinaire Hassan II. BP 6202, 10101 – Rabat, Maroc
  • 3Ecole Nationale des Sciences Appliquées d’Agadir, B.P. 1136, Agadir, Maroc

Keywords: Remote Sensing, IKONOS, High resolution satellite images, Cascade classifiers, Active Shape Model, Local Binary Patterns, Haar features, SURF, SVM, barchans dunes, desertification

Abstract. Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. Their study is of great interest for urban planners and geologists interested in desertification (Hugenholtz et al., 2012). In order to study them at a large scale, the use of remote sensing is necessary. Indeed, barchans can be part of barchan fields which can be composed of thousands of dunes (Elbelrhiti et al.2008). Our region of interest is located in the south of Morocco, near the city of Laayoune, where barchans are stretching over a 400 km corridor of sand dunes.

We used image processing techniques based on machine learning approaches to detect both the location and the outlines of barchan dunes. The process we developed combined two main parts: The first one consists of the detection of crescent shaped dunes in satellite images using a supervised learning method and the second one is the mapping of barchans contours (windward, brink and leeward) defining their 2D pattern.

For the detection, we started by image enhancement techniques using contrast adjustment by histogram equalization along with noise reduction filters. We then used a supervised learning method: We annotated the samples and trained a hierarchical cascade classifier that we tested with both Haar and LBP features (Viola et Jones, 2001; Liao et al., 2007). Then, we merged positive bounding boxes exceeding a defined overlapping ratio. The positive examples were then qualified to the second part of our approach, where the exact contours were mapped using an image processing algorithm: We trained an ASM (Active Shape Model) (Cootes et al., 1995) to recognize the contours of barchans. We started by selecting a sample with 100 barchan dunes with 30 landmarks (10 landmarks for each one of the 3 outlines). We then aligned the shapes using Procrustes analysis, before proceeding to reduce the dimensionality using PCA. Finally, we tested different descriptors for the profiles matching: HOG features were used to construct a multivariate Gaussian model, and then SURF descriptors were fed an SVM. The result was a recursive model that successfully mapped the contours of barchans dunes.

We experimented with IKONOS high resolution satellite images. The use of IKONOS high resolution satellite images proved useful not only to have a good accuracy, but also allowed to map the contours of barchans sand dunes with a high precision. Overall, the execution time of the combined methods was very satisfying.