Over recent decades, remote sensing has emerged as an effective tool for improving agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi-resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchical segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra-variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a single image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.
This is a preview of subscription content, access via your institution.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2010). Slic superpixels. École Polytechnique Fédéral de Lausssanne (EPFL), Tech Rep 149300.
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282.
Baatz, M., & Schäpe, A. (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII (pp. 12–23).
Baatz, M., Hoffmann, C., & Willhauck, G. (2008). Progressing from object-based to object-oriented image analysis. In T. Blaschke, S. Lang & G. Hay (Eds.), Object-based image analysis, lecture notes in geoinformation and cartography (pp. 29–42). Berlin: Springer.
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for gis-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3), 239–258.
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3), 233–249.
Chen, Q.X., Luo, J.C., Zhou, C.H., & Pei, T. (2003). A hybrid multi-scale segmentation approach for remotely sensed imagery. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (Vol. 6, pp. 3416–3419).
Chitiboi, T., Hennemuth, A., Tautz, L., Stolzmann, P., Donati, O.F., Linsen, L., & Hahn, H.K. (2013). Automatic detection of myocardial perfusion defects using object-based myocardium segmentation. In Computing in Cardiology Conference (CinC) (pp. 639–642).
Duveiller, G., & Defourny, P. (2010). A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing. Remote Sensing of Environment, 114(11), 2637–2650.
Espindola, G.M., Camara, G., Reis, I.A., Bins, L.S., & Monteiro, A.M. (2006). Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27(14), 3035–3040.
Felzenszwalb, P.F., & Huttenlocher, D.P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181.
Gao, Y., & Mas, J. (2008). A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. Proceedings of GEOBIA 2008 Pixels, Objects, Intelligence: Geographic Object-Based Image Analysis for the 21st Century, 2(1), 27–35.
Garcia-Pedrero, A., Gonzalo-Martin, C., Fonseca-Luengo, D., & Lillo-Saavedra, M. (2015). A GEOBIA methodology for fragmented agricultural landscapes. Remote Sensing, 7(1), 767–787.
Hay, G., & Castilla, G. (2008). Geographic object-based image analysis (geobia): A new name for a new discipline. In T. Blaschke, S. Lang & G. Hay (Eds.), Object-based image analysis, lecture notes in geoinformation and cartography (pp. 75–89). Berlin: Springer.
Homeyer, A., Schwier, M., & Hahn, H.K. (2010). A generic concept for object-based image analysis. In VISAPP (pp. 530–533).
Johnson, B., & Xie, Z. (2011). Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4), 473–483.
Kim, M., Madden, M., Warner, T.A., & et al. (2009). Forest type mapping using object-specific texture measures from multispectral ikonos imagery: segmentation quality and image classification issues. Photogrammetric Engineering and Remote Sensing, 75 (7), 819–829.
Levin, S. (1992). The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology, 73(6), 1943–1967.
Li, H., Gu, H., Han, Y., & Yang, J. (2008). An efficient multi-scale segmentation for high-resolution remote sensing imagery based on statistical region merging and minimum heterogeneity rule. In Earth Observation and Remote Sensing Applications, International Workshop on EORSA (pp. 1–6). IEEE.
Lucchi, A., Smith, K., Achanta, R., Knott, G., & Fua, P. (2012). Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features. IEEE Transactions on Medical Imaging, 31(2), 474–486.
Maxwell, T., & Zhang, Y. (2005). A fuzzy logic approach to optimization of segmentation of object-oriented classification. In Proceedings of SPIE 50th annual meeting - optics & photonics (Vol. 5909, pp. 1–11).
Mynt, S.W., Gobera, P., Brazela, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixels vs object-based classif ication of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161.
Ozdogan, M., Yang, Y., Allez, G., & Cervantes, C. (2010). Remote sensing of irrigated agriculture: opportunities and challenges. Remote Sensing, 2(9), 2274–2304.
Paul, T., & Philip, H. (2000). High spatial resolution remote sensing data for forest ecosystem classification: an examination of spatial scale. Remote Sensing of Environment, 72(3), 268–289.
Peña-Barragan, J., Ngugi, M., Plant, R., & Six, J. (2011). Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115(6), 1301–1316.
Ren, X., & Malik, J. (2003). Learning a classification model for segmentation. In Computer vision, 2003. ninth IEEE international conference on proceedings (Vol. 1, pp. 10–17). IEEE.
Trias-Sanz, R., Stamon, G., & Louchet, J. (2008). Using colour, texture, and hierarchial segmentation for high-resolution remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), 156–168.
Vedaldi, A., & Soatto, S. (2008). Quick shift and kernel methods for mode seeking. In Computer vision–ECCV 2008 (pp. 705–718). Springer.
Vieira, M., Formaggio, A., Rennó, C., Atzberger, C., Aguiar, D., & Mello, M. (2012). Object based image analysis and data mining applied to a remotely sensed landsat time-series to map sugarcane over large areas. Remote Sensing of Environment, 123, 553–562.
Woodcock, C.E., & Strahler, A.H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment, 21(3), 311–332.
Yan, G., Mas, J., Maathuis, B., Xiangmin, Z., & Van Dijk, P. (2006). Comparison of pixel-based and object-oriented image classification approachesa case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18), 4039–4055.
Zhong, C., Zhongmin, Z., DongMei, Y., & Renxi, C. (2005). Multi-scale segmentation of the high resolution remote sensing image. In Proccedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3682–3684).
Zhou, W., & Troy, A. (2009). Development of an object-based framework for classifying and inventorying human-dominated forest ecosystems. International Journal of Remote Sensing, 30(23), 6343–6360.
A. García-Pedrero (grant 216146) and D. Fonseca-Luengo acknowledge the support for the realization of their doctoral thesis to the Mexican National Council of Science and Technology (CONACyT) and the National Commission for Scientific and Technological Research (CONICYT), respectively.
This work has been funded by the Centro de Recursos Hídricos para la Agricultura y la Minería (CONICYT/FONDAP/1513001).
About this article
Cite this article
Gonzalo-Martín, C., Lillo-Saavedra, M., Menasalvas, E. et al. Local optimal scale in a hierarchical segmentation method for satellite images. J Intell Inf Syst 46, 517–529 (2016). https://doi.org/10.1007/s10844-015-0365-4
- Remote sensing
- Hierarchical segmentation
- High resolution images