+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn

+ Translate
+ Recently Requested

Hierarchical image segmentation based on similarity of NDVI time series

Hierarchical image segmentation based on similarity of NDVI time series

Remote Sensing of Environment 112(2): 506-521

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover–landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.

(PDF emailed within 0-6 h: $19.90)

Accession: 021131240

Download citation: RISBibTeXText

DOI: 10.1016/j.rse.2007.05.018

Related references

Hierarchical image segmentation using local and adaptive similarity rules. International Journal of Remote Sensing 13(8): 1559-1570, 1992

Eco-climatic image segmentation based on time series. Communications in Agricultural and Applied Biological Sciences 70(2): 165-168, 2005

Hierarchical color image region segmentation for content-based image retrieval system. IEEE Transactions on Image Processing 9(1): 156-162, 2008

Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity. International Journal of Remote Sensing 26(24): 5535-5554, 2005

Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sensing of Environment 99(3): 341-356, 2005

Watershed-based hierarchical SAR image segmentation. International Journal of Remote Sensing 20(17): 3377-3390, 1999

Supervised SAR Image MPM Segmentation Based on Region-Based Hierarchical Model. IEEE Geoscience and Remote Sensing Letters 3(4): 517-521, 2006

Context-Based Hierarchical Unequal Merging for SAR Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing 51(2): 995-1009, 2013

Hierarchical Image Segmentation Based on Iterative Contraction and Merging. IEEE Transactions on Image Processing 26(5): 2246-2260, 2017

Hierarchical image segmentation based on watershed filtering and fuzzy cluster. Di 1 Jun Yi Da Xue Xue Bao 24(3): 329-331, 2004

A variational framework for multiregion pairwise-similarity-based image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(8): 1400-1414, 2008

Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia). Remote Sensing 7(10): 13005-13028, 2015

Three-dimensional CT liver image segmentation based on hierarchical contextual active contour. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 31(2): 405-412, 2015

Image segmentation based on hierarchical belief propagation with variable weighting parameters. Optik - International Journal for Light and Electron Optics 125(3): 1158-1163, 2014

Hierarchical segmentation-based image coding using hybrid quad-binary trees. IEEE Transactions on Image Processing 18(6): 1284-1291, 2009