Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-22T13:27:23.888Z Has data issue: false hasContentIssue false

Extracting multilayer networks from Sentinel-2 satellite image time series

Published online by Cambridge University Press:  17 January 2020

Roberto Interdonato*
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Raffaele Gaetano
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Danny Lo Seen
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Mathieu Roche
Affiliation:
CIRAD, UMR TETIS, Maison de la Télédétection, 500 Rue J.-F. Breton, F-34000Montpellier, France UMR TETIS, University of Montpellier, AgroParisTech, CIRAD, CNRS, IRSTEA, F-34000Montpellier, France (e-mails: raffaele.gaetano@cirad.fr; danny.lo_seen@cirad.fr; mathieu.roche@cirad.fr)
Giuseppe Scarpa
Affiliation:
Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125Naples, Italy (e-mail: giscarpa@unina.it)
*
*Corresponding author. Email: roberto.interdonato@cirad.fr

Abstract

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

Type
Research Article
Copyright
© Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baatz, M., & Schäpe, A. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte geographische informationsverarbeitung xii, 58, 1223.Google Scholar
Bégué, A., Arvor, D., Lelong, C., Vintrou, E., & Simoes, M. (2015). Agricultural systems studies using remote sensing. In Thenkabail, P. S. (Ed.), Land resources monitoring, modeling, and mapping with remote sensing (remote sensing handbook, 2) (pp. 113130). Boca Raton: CRC Press.Google Scholar
Bellón, B., Bégué, A., Lo Seen, D., de Almeida, C., & Simões, M. (2017). A remote sensing approach for regional-scale mapping of agricultural land-use systems based on NDVI time series. Remote Sensing, 9(6), 600.CrossRefGoogle Scholar
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 216.CrossRefGoogle Scholar
Bousquet, F., Bakam, I., Proton, H., & Le Page, C. (1998). Cormas: Common-Pool Resources and Multi-agent Systems. Proceedings of the International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE), Vol. 2, Lecture Notes in Computer Science 1416, 826837.CrossRefGoogle Scholar
Chen, G., Weng, Q., Hay, G. J., & He, Y. (2018). Geographic object-based image analysis (geobia): emerging trends and future opportunities. Giscience & Remote Sensing, 55(2), 159182.CrossRefGoogle Scholar
Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247261.CrossRefGoogle Scholar
Costanza, R. (1987). Simulation modeling on the macintosh using stella. Bioscience, 37(2), 129132.CrossRefGoogle Scholar
De Domenico, M., Lancichinetti, A., Arenas, A., & Rosvall, M. (2015). Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X, 5(March), 011027.Google Scholar
DeFries, R., & Rosenzweig, C. (2010). Toward a whole-landscape approach for sustainable land use in the tropics. Proceedings of the National Academy of Sciences.CrossRefGoogle Scholar
Degenne, P., & Lo Seen, D. (2016). Ocelet: Simulating processes of landscape changes using interaction graphs. Softwarex, 5, 8995.CrossRefGoogle Scholar
Degenne, P., Lo Seen, D., Parigot, D., Forax, R., Tran, A., Ait Lahcen, A., Curé, O., & Jeansoulin, R. (2009). Design of a Domain Specific Language for modelling processes in landscapes. Ecological Modelling, 220(24), 35273535.CrossRefGoogle Scholar
Degenne, P., Ait Lahcen, A., Curé, O., Forax, R., Parigot, D., & Lo Seen, D. (2010, July). Modelling the environment using graphs with behaviour: Do you speak Ocelet? iEMSs 2010, 8 pages.Google Scholar
Edler, D., Bohlin, L., & Rosvall, M. (2017). Mapping higher-order network flows in memory and multilayer networks with infomap. Corr., abs/1706.04792.CrossRefGoogle Scholar
Gaetano, R., Scarpa, G., & Sziranyi, T. (2010). Graph-based analysis of Textured Images for Hierarchical Segmentation. In British Machine Vision Conference, BMVC 2010, 47(7–2): 21292141.Google Scholar
Gaetano, R., Scarpa, G., & Poggi, G. (2009). Hierarchical texture-based segmentation of multiresolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing.CrossRefGoogle Scholar
Guttler, F., Ienco, D., Nin, J., Teisseire, M., & Poncelet, P. (2017). A graph-based approach to detect spatiotemporal dynamics in satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing.CrossRefGoogle Scholar
Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D. & Rodes, I. (2017). Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sensing, 9(1), 95.CrossRefGoogle Scholar
Interdonato, R., Ienco, D., Gaetano, R., & Ose, K. (2019). Duplo: A dual view point deep learning architecture for time series classification. ISPRS Journal of Photogrammetry and Remote Sensing, 149, 91104.CrossRefGoogle Scholar
Jahel, C., Baron, C., Vall, E., Karambiri, M., Castets, M., Coulibaly, K., Bégué, A., & Seen, D. L. (2017). Spatial modelling of agro-ecosystem dynamics across scales: A case in the cotton region of west-burkina faso. Agricultural Systems, 157, 303315.CrossRefGoogle Scholar
Jahel, C., Vall, E., Rodriguez, Z., Bégué, A, Baron, C., Augusseau, X., & Seen, D. L. (2018). Analysing plausible futures from past patterns of land change in west burkina faso. Land Use Policy, 71, 6074.CrossRefGoogle Scholar
Khiali, L., Ienco, D., & Teisseire, M. (2018). Object-oriented satellite image time series analysis using a graph-based representation. Ecological Informatics, 43, 5264.CrossRefGoogle Scholar
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Mutilayer networks. Journal of Complex Networks, 2(3), 203271.CrossRefGoogle Scholar
Kolecka, N., Ginzler, C., Pazur, R., Price, B., & Verburg, P. H. (2018). Regional scale mapping of grassland mowing frequency with sentinel-2 time series. Remote Sensing, 10(8), 1221.CrossRefGoogle Scholar
Lebourgeois, V., Dupuy, S., Vintrou, E., Ameline, M., Butler, S., & Bégué, A. (2017). A combined random forest and OBIA classification scheme for mapping smallholder agriculture at different nomenclature levels using multisource data (simulated sentinel-2 time series, VHRS and DEM). Remote Sensing, 9(3), 259.CrossRefGoogle Scholar
Leenhardt, D., Angevin, F., Biarnès, A., Colbach, N., & Mignolet, C. (2010). Describing and locating cropping systems on a regional scale. A review. Agronomy for Sustainable Development, 30(1), 131138.CrossRefGoogle Scholar
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876878.CrossRefGoogle ScholarPubMed
Ross, T. (2009). Fuzzy logic with engineering applications. Fuzzy Logic with Engineering Applications (3rd ed.), 01.Google Scholar
Rouse, Jr, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts. Nasa Special Publication, 351, 309.Google Scholar
Scarpa, G., Gaetano, R., Haindl, M., & Zerubia, J. (2009). Hierarchical multiple Markov chain model for unsupervised texture segmentation. IEEE Transactions on Image Processing, 18(8), 18301843.CrossRefGoogle ScholarPubMed
Schmidt-Laine, C, & Pave, A. (2002). Environment: Modelling and models to understand, to manage and to decide in an interdisciplinary context. Natures Sciences Societes, 10(01), 525.CrossRefGoogle Scholar
Tobler, W. (1970). A computer movie simulating urban growth in the detroit region. Economic Geography, 46(2), 234240.CrossRefGoogle Scholar
Verburg, P H., van de Steeg, J., Veldkamp, A., & Willemen, L. (2009). From land cover change to land function dynamics: A major challenge to improve land characterization. Journal of Environmental Management, 90(3), 13271335.CrossRefGoogle Scholar
Verburg, P. H., Mertz, O., Erb, K.-H., Haberl, H., & Wu, W. (2013). Land system change and food security: Towards multi-scale land system solutions. Current Opinion in Environmental Sustainability, 5(5), 494502. Human settlements and industrial systems.CrossRefGoogle ScholarPubMed
Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4, 2240.CrossRefGoogle Scholar
Zhu, X. X., Tuia, D., Mou, L., Xia, G., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 836.CrossRefGoogle Scholar