Abstract
We study the pattern spectra in context of soil structure analysis. Good soil structure is vital for sustainable crop growth. Accurate and fast measuring methods can contribute greatly to soil management decisions. However, the current in-field approaches contain a degree of subjectivity, while obtaining quantifiable results through laboratory techniques typically involves sieving the soil which is labour- and time-intensive. We aim to replace this physical sieving process through image analysis, and investigate the effectiveness of pattern spectra to capture the size distribution of the soil aggregates. We calculate the pattern spectra from partitioning hierarchies in addition to the traditional max-tree. The study is posed as an image retrieval problem, and confirms the ability of pattern spectra and suitability of different partitioning trees to re-identify soil samples in different arrangements and scales.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Beare, M.H., Bruce, R.R.: A comparison of methods for measuring water-stable aggregates: implications for determining environmental effects on soil structure. Geoderma 56(1–4), 87–104 (1993)
Bianconi, F., Di Maria, F., Micale, C., Fernández, A., Harvey, R.: Grain-size assessment of fine and coarse aggregates through bipolar area morphology. Mach. Vis. Appl. 26(6), 775–789 (2015)
Bosilj, P., Aptoula, E., Lefèvre, S., Kijak, E.: Retrieval of remote sensing images with pattern spectra descriptors. ISPRS Int. J. Geo-Inf. 5(12), 228 (2016)
Bosilj, P., Damodaran, B.B., Aptoula, E., Mura, M.D., Lefèvre, S.: Attribute profiles from partitioning trees. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds.) ISMM 2017. LNCS, vol. 10225, pp. 381–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57240-6_31
Breen, E.J., Jones, R.: Attribute openings, thinnings, and granulometries. Comput. Vis. Image Underst. 64(3), 377–389 (1996)
Bronick, C.J., Lal, R.: Soil structure and management: a review. Geoderma 124(1–2), 3–22 (2005)
Buscombe, D., Masselink, G.: Grain-size information from the statistical properties of digital images of sediment. Sedimentology 56(2), 421–438 (2009)
Cavallaro, G., Falco, N., Dalla Mura, M., Benediktsson, J.: Automatic attribute profiles. IEEE Trans. Image Process. 26(4), 1859–1872 (2017)
Cousty, J., Najman, L., Kenmochi, Y., Guimarães, S.: Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps. J. Math. Imaging Vis. 60(4), 479–502 (2018)
Doulamis, A., Doulamis, N., Maragos, P.: Generalized multiscale connected operators with applications to granulometric image analysis. In: ICIP, vol. 3, pp. 684–687. IEEE (2001)
Frančišković-Bilinski, S., Bilinski, H., Vdović, N., Balagurunathan, Y., Dougherty, E.R.: Application of image-based granulometry to siliceous and calcareous estuarine and marine sediments. Estuar. Coast. Shelf Sci. 58(2), 227–239 (2003)
Graham, D.J., Reid, I., Rice, S.P.: Automated sizing of coarse-grained sediments: image-processing procedures. Math. Geol. 37(1), 1–28 (2005)
Guimarães, R.M.L., Ball, B.C., Tormena, C.A.: Improvements in the visual evaluation of soil structure. Soil Use Manag. 27(3), 395–403 (2011)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. 11(7), 701–716 (1989)
Matheron, G.: Random Sets and Integral Geometry. Wiley, New York (1975)
Monasse, P., Guichard, F.: Scale-space from a level lines tree. J. Vis. Commun. Image Represent. 11(2), 224–236 (2000)
Mora, C.F., Kwan, A.K.H., Chan, H.C.: Particle size distribution analysis of coarse aggregate using digital image processing. Cem. Concr. Res. 28(6), 921–932 (1998)
Mueller, L., et al.: Visual assessment of soil structure: evaluation of methodologies on sites in canada, china and germany: Part I: comparing visual methods and linking them with soil physical data and grain yield of cereals. Soil Tillage Res. 103(1), 178–187 (2009)
Pina, P., Lira, C., Lousada, M.: In-situ computation of granulometries of sedimentary grains-some preliminary results. J. Coast. Res. 64, 1727–1730 (2011)
Salehizadeh, M., Sadeghi, M.T.: Size distribution estimation of stone fragments via digital image processing. In: Bebis, G., et al. (eds.) ISVC 2010. LNCS, vol. 6455, pp. 329–338. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17277-9_34
Salembier, P., Oliveras, A., Garrido, L.: Anti-extensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)
Soille, P.: On genuine connectivity relations based on logical predicates. In: ICIAP, pp. 487–492. IEEE (2007)
Soille, P.: Constrained connectivity for hierarchical image partitioning and simplification. IEEE Trans. Pattern Anal. 30(7), 1132–1145 (2008)
Urbach, E.R., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Trans. Pattern Anal. 29(2), 272–285 (2007)
Urbach, E.R., Wilkinson, M.H.F.: Shape-only granulometries and grey-scale shape filters. In: ISMM, pp. 305–314 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bosilj, P., Gould, I., Duckett, T., Cielniak, G. (2019). Pattern Spectra from Different Component Trees for Estimating Soil Size Distribution. In: Burgeth, B., Kleefeld, A., Naegel, B., Passat, N., Perret, B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science(), vol 11564. Springer, Cham. https://doi.org/10.1007/978-3-030-20867-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-20867-7_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20866-0
Online ISBN: 978-3-030-20867-7
eBook Packages: Computer ScienceComputer Science (R0)