Segmentation of Optical Remote Sensing Images for Detecting Homogeneous Regions in Space and Time


With the amount of multitemporal and multiresolution images growing exponentially, the number of image segmentation applications is recently increasing and, simultaneously, new challenges arise. Hence, there is a need to explore new segmentation concepts and techniques that make use of the data temporality. This study describes a spatio-temporal segmentation that adapts the traditional region growing technique to detect homogeneous regions in space and time in optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping measure as the homogeneity criterion. Study cases on high temporal resolution for sequences of MODIS and Landsat-8 OLI vegetation indices products and comparisons with other distance measurements provided satisfactory outcomes.


Spatio-temporal segmentation; Image Processing; Dynamic Time Warping

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ADAMS, R.; BISCHOF, L. Seeded region growing. Pattern Analysis and Machine Intelligence, IEEE Transactions on, IEEE, v. 16, n. 6, p. 641–647, 1994.

BINS, L. S.; FONSECA, L. M. G.; ERTHAL, G. J.; II, F. M. Satellite imagery segmentation: a region growing approach. Simpósio Brasileiro de Sensoriamento Remoto, Imagem Multimidia, São Paulo. Proceedings, CD Salvador, Bahia, Brazil, v. 8, n. 1996, p. 677–680, 1996.

BLASCHKE, T. Towards a framework for change detection based on image objects. Göttinger Geographische Abhandlungen, v. 113, p. 1–9, 2005.

BLASCHKE, T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, v. 65, n. 1, p. 2–16, 2010.

BONTEMPS, S.; BOGAERT, P.; TITEUX, N.; DEFOURNY, P. An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution. Remote Sensing of Environment, Elsevier, v. 112, n. 6, p. 3181–3191, 2008.

BORIAH, S. Time series change detection: algorithms for land cover change. Tese (Doutorado) — University of Minnesota, 160 p., 2010.

BOULILA, W.; FARAH, I. R.; ETTABAA, K. S.; SOLAIMAN, B.; GHÉZALA, H. B. A data mining based approach to predict spatiotemporal changes in satellite images. International Journal of Applied Earth Observation and Geoinformation, Elsevier, v. 13, n. 3, p. 386–395, 2011.

BRAZIL. Sectoral plan for climate mitigation and adaptation. Ministry of agriculture, Livestock and Food Supply. Brasilia, 2011.

BRUZZONE, L.; SMITS, P. C.; TILTON, J. C. Foreword special issue on analysis of multitemporal remote sensing images. Geoscience and Remote Sensing, IEEE Transactions on, IEEE, v. 41, n. 11, p. 2419–2422, 2003.

COSTA, W. S.; FONSECA, L. M. G.; KORTING, T. S.; SIMÕES, M. G.; BENDINI, H. N.; SOUZA, R. C. M. Segmentation of optical remote sensing images for detecting homogeneous regions in space and time. In: XVIII Brazilian Symposium on GeoInformatics (GeoInfo). Proceedings. Salvador, National Institute for Space Research, p. 40-51, 2017.

CHU, S.; KEOGH, E.; HART, D.; PAZZANI, M. Iterative deepening dynamic time warping for time series. In: Proceedings of the 2002 SIAM International Conference on Data Mining. Philadelphia, PA: Society forIndustrial and Applied Mathematics, p. 195–212, 2002.

DESCLÉE, B.; BOGAERT, P.; DEFOURNY, P. Forest change detection by statistical object-based method. Remote Sensing of Environment, Elsevier, v. 102, n. 1, p. 1–11, 2006.

DEY, V.; ZHANG, Y.; ZHONG, M. A review on image segmentation techniques with remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, ISPRS, Viena, Austria, XXXVIII, p. 31–42, July 2010.

DRAGUT, L.; CSILLIK, O.; EISANK, C.; TIEDE, D. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, v. 88, p. 119–127, 2014.

DRAGUT, L.; TIEDE, D.; LEVICK, S. R. ESP: a tool to estimate scale˘ parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, Taylor & Francis, v. 24, n. 6, p. 859–871, 2010.

DURO, D.; FRANKLIN, S.; DUBÉ, M. Hybrid object-based change detection and hierarchical image segmentation for thematic map updating. Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, v. 79, n. 3, p. 259–268, 2013.

EECKHAUT, M. V. D.; KERLE, N.; POESEN, J.; HERVáS, J. Object-oriented identification of forested landslides with derivatives of single pulse lidar data. Geomorphology, v. 173–174, p. 30–42, 2012.

FREITAS, R. d.; ARAI, E.; ADAMI, M.; FERREIRA, A. S.; SATO, F. Y.; SHIMABUKURO, Y. E.; ROSA, R. R.; ANDERSON, L. O.; RUDORFF, B. F. T. Virtual laboratory of remote sensing time series: visualization of MODIS EVI2 data set over South America. Journal of Computational Interdisciplinary Sciences, v. 2, n. 1, p. 57–68, 2011.

GOMEZ, C.; WHITE, J. C.; WULDER, M. A. Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation. Remote Sensing of Environment, Elsevier, v. 115, n. 7, p. 1665–1679, 2011.

HARALICK, R. M.; SHAPIRO, L. G. Image segmentation techniques. In: Technical Symposium East. Arlington, VA: International Society for Optics and Photonics, 1985.

HUETE, A.; DIDAN, K.; MIURA, T.; RODRIGUEZ, E. P.; GAO, X.; FERREIRA, L. G. Overview of the radiometric and biophysical performance of the modis vegetation indices. Remote Sensing of Environment, Elsevier, v. 83, n. 1, p. 195–213, 2002.

IM, J.; JENSEN, J.; TULLIS, J. Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing, Taylor & Francis, v. 29, n. 2, p. 399–423, 2008.

JIANG, Z.; HUETE, A. R.; DIDAN, K.; MIURA, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, Elsevier, v. 112, n. 10, p. 3833–3845, 2008.

JUSTICE, C.; TOWNSHEND, J.; VERMOTE, E.; MASUOKA, E.; WOLFE, R.; SALEOUS, N.; ROY, D.; MORISETTE, J. An overview of MODIS land data processing and product status. Remote sensing of Environment, Elsevier, v. 83, n. 1, p. 3–15, 2002.

LAMBIN, E. F.; LINDERMAN, M. Time series of remote sensing data for land change science. Geoscience and Remote Sensing, IEEE Transactions on, IEEE, v. 44, n. 7, p. 1926–1928, 2006.

MAUS, V.; CAMARA, G.; CARTAXO, R.; RAMOS, F. M.; SANCHEZ, A.; RIBEIRO, G. Q. Open boundary dynamic time warping for satellite image time series classification. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, p. 3349–3352, 2015.

NIEMEYER, I.; MARPU, P.; NUSSBAUM, S. Change detection using object features. In: BLASCHKE, T.; LANG, S.; HAY, G. (Ed.). Object-Based Image Analysis. Springer Berlin Heidelberg, (Lecture Notes in Geoinformation and Cartography). p. 185–201, 2008.

OLIVEIRA, J. C. d. Índice para avaliação de segmentação (IAVAS): uma aplicação em agricultura. Dissertação (Mestrado - Instituto Nacional de Pesquisas Espaciais, 160 p. São José dos Campos, 2002.

PAPE, A. D.; FRANKLIN, S. E. MODIS-based change detection for Grizzly Bear habitat mapping in Alberta. Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, v. 74, n. 8, p. 973–985, 2008.

PETITJEAN, F.; INGLADA, J.; GANÇARSKI, P. Clustering of satellite image time series under time warping. In: IEEE. Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the. Trento, Italy, 2011.

PETITJEAN, F.; INGLADA, J.; GANÇARSKI, P. Satellite image time series analysis under time warping. Geoscience and Remote Sensing, IEEE Transactions on, IEEE, v. 50, n. 8, p. 3081–3095, 2012.

SAKOE, H.; CHIBA, S. A dynamic programming approach to continuous speech recognition. In: Proceedings of the seventh international congress on acoustics. Budapest: Akademiai Kiado, v. 3, p. 65–69, 1971.

SAKOE, H.; CHIBA, S. Dynamic programming algorithm optimization for spoken word recognition. In: Acoustics, Speech and Signal Processing, IEEE Transactions on. New York, NY: IEEE, v. 26, n. 1, p. 43–49, 1978.

SCHIEWE, J. Segmentation of high-resolution remotely sensed data-concepts, applications and problems. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, Natural Resources Canada, v. 34, n. 4, p. 380–385, 2002.

THOMPSON, J. A.; LEES, B. G. Applying object-based segmentation in the temporal domain to characterise snow seasonality. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, v. 97, p. 98–110, 2014.

TUCKER, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, Elsevier, v. 8, n. 2, p. 127–150, 1979.

TUCKER, C. J.; PINZON, J. E.; BROWN, M. E.; SLAYBACK, D. A.; PAK, E. W.; MAHONEY, R.; VERMOTE, E. F.; SALEOUS, N. E. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, Taylor & Francis, v. 26, n. 20, p. 4485–4498, 2005.


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