Satellite Image Time Series Analysis Under Time Warping

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim at providing a coverage of the Earth every few days with high spatial resolution. In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling, and one will need to compare time series with different lengths. In this paper, we present an approach to image time series analysis which is able to deal with irregularly sampled series and which also allows the comparison of pairs of time series where each element of the pair has a different number of samples. We present the dynamic time warping from a theoretical point of view and illustrate its capabilities with two applications to real-time series.

[1]  C. Munyati,et al.  Wetland change detection on the Kafue Flats, Zambia, by classification of a multitemporal remote sensing image dataset , 2000 .

[2]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[3]  Gérard Dedieu,et al.  A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images , 2010 .

[4]  Lorenzo Bruzzone,et al.  A neural-statistical approach to multitemporal and multisource remote-sensing image classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[5]  G. W. Hughes,et al.  Minimum Prediction Residual Principle Applied to Speech Recognition , 1975 .

[6]  Pierre Gançarski,et al.  A global averaging method for dynamic time warping, with applications to clustering , 2011, Pattern Recognit..

[7]  Kai-Kuang Ma,et al.  Multitemporal Image Change Detection Using Undecimated Discrete Wavelet Transform and Active Contours , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Sébastien Lefèvre,et al.  Fast quasi-flat zones filtering using area threshold and region merging , 2013, J. Vis. Commun. Image Represent..

[9]  Eamonn J. Keogh,et al.  Scaling and time warping in time series querying , 2005, The VLDB Journal.

[10]  W. Cohen,et al.  An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery , 1998 .

[11]  J. R. Jensen.  Urban Change Detection Mapping Using Landsat Digital Data , 1981 .

[12]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[13]  Turgay Çelik,et al.  Unsupervised multiscale change detection in multitemporal synthetic aperture radar images , 2009, 2009 17th European Signal Processing Conference.

[14]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[15]  S. Goetz,et al.  Large-Scale Patterns of Forest Succession as Determined by Remote Sensing , 1991 .

[16]  Arnt-Børre Salberg,et al.  Land Cover Classification of Cloud-Contaminated Multitemporal High-Resolution Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Larry S. Davis,et al.  Towards 3-D model-based tracking and recognition of human movement: a multi-view approach , 1995 .

[18]  Gabriele Moser,et al.  Unsupervised change detection methods for remote sensing images , 2002, SPIE Remote Sensing.

[19]  Dimitrios Gunopulos,et al.  Indexing Multidimensional Time-Series , 2004, The VLDB Journal.

[20]  Shane S. Sturrock,et al.  Time Warps, String Edits, and Macromolecules – The Theory and Practice of Sequence Comparison . David Sankoff and Joseph Kruskal. ISBN 1-57586-217-4. Price £13.95 (US$22·95). , 2000 .

[21]  M. Ridd,et al.  A Comparison of Four Algorithms for Change Detection in an Urban Environment , 1998 .

[22]  Armond T. Joyce,et al.  Evaluation of land cover change detection techniques using Landsat MSS data , 1982 .

[23]  Christophe Rigotti,et al.  Unsupervised Spatiotemporal Mining of Satellite Image Time Series Using Grouped Frequent Sequential Patterns , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Francesca Bovolo,et al.  A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[25]  R. D. Johnson,et al.  Change vector analysis: A technique for the multispectral monitoring of land cover and condition , 1998 .

[26]  Eamonn J. Keogh,et al.  Towards parameter-free data mining , 2004, KDD.

[27]  R. Manmatha,et al.  Word image matching using dynamic time warping , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[28]  Joseph B. Kruskall,et al.  The Symmetric Time-Warping Problem : From Continuous to Discrete , 1983 .

[29]  William Salas,et al.  Fourier analysis of multi-temporal AVHRR data applied to a land cover classification , 1994 .

[30]  Pierre Gançarski,et al.  Spatio-temporal Quasi-Flat Zones for Morphological Video Segmentation , 2011, ISMM.

[31]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[32]  Alan H. Strahler,et al.  Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change pro , 1994 .

[33]  Pierre Soille,et al.  Constrained Connectivity and Transition Regions , 2009, ISMM.

[34]  Hiroshi Hanaizumi,et al.  Change Detection From Remotely Sensed Multi-temporal Images Using Spatial Segmentation , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[35]  Jordi Inglada,et al.  A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[36]  W. Todd.  Urban and regional land use change detected by using Landsat data , 1977 .

[37]  Adriano Camps,et al.  A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer Observations , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[38]  D. Lu,et al.  Change detection techniques , 2004 .

[39]  P. Howarth,et al.  Time-Series Analysis of Medium-Resolution, Multisensor Satellite Data for Identifying Landscape Change , 2006 .

[40]  Francesca Bovolo.  A Multilevel Parcel-Based Approach to Change Detection in Very High Resolution Multitemporal Images , 2009, IEEE Geosci. Remote. Sens. Lett..

[41]  A. Shiryayev.  On Tables of Random Numbers , 1993 .

[42]  Hiroaki Sakoe,et al.  A Dynamic Programming Approach to Continuous Speech Recognition , 1971 .

[43]  G. Foody.  Monitoring the magnitude of land-cover change around the southern limits of the Sahara , 2001 .

[44]  Germain Forestier,et al.  Analysing Satellite Image Time Series by Means of Pattern Mining , 2010, IDEAL.

[45]  Stephen S. Young,et al.  Land-cover change analysis of China using global-scale Pathfinder AVHRR Landcover (PAL) data, 1982?92 , 2001 .

[46]  Knut Conradsen,et al.  Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies , 1998 .

[47]  George M. Church,et al.  Aligning gene expression time series with time warping algorithms , 2001, Bioinform..

[48]  Philippe Bolon,et al.  On Extracting Evolutions from Satellite Image Time Series , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[49]  C. Jha,et al.  Digital change detection of forest conversion of a dry tropical Indian forest region , 1994 .

[50]  Nicolas Passat,et al.  Spatio-temporal reasoning for the classification of satellite image time series , 2012, Pattern Recognit. Lett..

[51]  Pierre Soille,et al.  Constrained connectivity for hierarchical image partitioning and simplification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Tommi S. Jaakkola,et al.  A new approach to analyzing gene expression time series data , 2002, RECOMB '02.