A novel change detection method using remotely sensed image time series value and shape based dynamic time warping

References

• Allbed A, Kumar L, Sinha P. 2018. Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia. Geocarto Int. 33(8):830–846.
   Google Scholar
• Awty-Carroll K, Bunting P, Hardy A, Bell G. 2019. An evaluation and comparison of four dense time series change detection methods using simulated data. Remote Sens. 11(23):2779–2808.
   Google Scholar
• Bazi Y, Melgani F, Al-Sharari HD. 2010. Unsupervised change detection in multispectral remotely sensed imagery with level set methods. IEEE Trans Geosci Remote Sens. 48(8):3178–3187.
   Web of Science ®Google Scholar
• Ben Abbes A, Bounouh O, Farah IR, de Jong R, Martínez B. 2018. Comparative study of three satellite image time-series decomposition methods for vegetation change detection. Eur J Remote Sens. 51(1):607–615.
   Web of Science ®Google Scholar
• Bovolo F, Bruzzone L, Solano-Correa YT. 2018. Multitemporal analysis of remotely sensed image data. [place unknown]: Elsevier.
   Google Scholar
• Bruzzone L, Prieto DF. 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Trans Geosci Remote Sens. 38(3):1171–1182.
   Web of Science ®Google Scholar
• Bullock EL, Woodcock CE, Holden CE. 2020. Improved change monitoring using an ensemble of time series algorithms. Remote Sens Environ. 238:1–53.
   Web of Science ®Google Scholar
• Byun Y, Han Y, Chae T. 2015. Image fusion-based change detection for flood extent extraction using bi-temporal very high-resolution satellite images. Remote Sens. 7(8):10347–10363.
   Google Scholar
• Carvalho Júnior OA, Guimarães RF, Gillespie AR, Silva NC, Gomes RAT. 2011. A new approach to change vector analysis using distance and similarity measures. Remote Sens. 3(11):2473–2493.
   Google Scholar
• Celik T. 2009. Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci Remote Sens Lett. 6(4):772–776.
   Web of Science ®Google Scholar
• Chen J, Chen J, Liu H, Peng S. 2018. Detection of cropland change using multi-harmonic based phenological trajectory similarity. Remote Sens. 10(7):1020–1040.
   Google Scholar
• Chen J, Lu M, Chen X, Chen J, Chen L. 2013. A spectral gradient difference based approach for land cover change detection. ISPRS J Photogramm Remote Sens. 85:1–12.
   Web of Science ®Google Scholar
• Chen Q, Chen Y. 2016. Multi-feature object-based change detection using self-adaptiveweight change vector analysis. Remote Sens. 8(7):549–568.
   Google Scholar
• Constantinopoulos C, Titsias MK, Likas A. 2006. Bayesian feature and model selection for Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell. 28(6):1013–1018.
   PubMed Web of Science ®Google Scholar
• De Carvalho OA, Meneses PR. 2000. Spectral correlation mapper (SCM): an improvement on the spectral angle mapper (SAM). Proc 9^th Airborne Earth Sci Work Pasadena, CA, USA, p. 23–25. Febr.
   Google Scholar
• Ghosh A, Mishra NS, Ghosh S. 2011. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf Sci (Ny). 181(4):699–715.
   Web of Science ®Google Scholar
• Goward SN, Markham B, Dye DG, Dulaney W, Yang J. 1991. Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sens Environ. 35(2-3):257–277.
   Web of Science ®Google Scholar
• Guan X, Huang C, Liu G, Meng X, Liu Q. 2016. Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens. 8(1):19–44.
   Google Scholar
• Hao M, Tan M, Zhang H. 2019. A change detection framework by fusing threshold and clustering methods for optical medium resolution remote sensing images. Eur J Remote Sens. 52(1):96–106.
   Web of Science ®Google Scholar
• He P, Shi W, Zhang H, Hao M. 2014. A novel dynamic threshold method for unsupervised change detection from remotely sensed images. Remote Sens Lett. 5(4):396–403.
   Web of Science ®Google Scholar
• Hu Y, Dong Y. Batunacun 2018. An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS J Photogramm Remote Sens. 146(10):347–359.
   Google Scholar
• Hussain M, Chen D, Cheng A, Wei H, Stanley D. 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens. 80:91–106.
   Web of Science ®Google Scholar
• Jamali S, Jönsson P, Eklundh L, Ardö J, Seaquist J. 2015. Remote Sensing of Environment Detecting changes in vegetation trends using time series segmentation. Remote Sens Environ. 156:182–195.
   Web of Science ®Google Scholar
• Ji S, Shen Y, Lu M, Zhang Y. 2019. Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sens. 11(11):1343–1363.
   Google Scholar
• Kamilaris A, Prenafeta-Boldú FX. 2018. Deep learning in agriculture: A survey. Comput Electron Agric. 147(February):70–90.
   Google Scholar
• Kapur JN, Sahoo PK, Wong AKC. 1985. A new method for grey-level picture thresholding using the entropy of the histogram. Comput Vision, Graph Image Process. 29(3):273–285.
   Google Scholar
• Kennedy RE, Yang Z, Cohen WB. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sens Environ. 114(12):2897–2910.
   Web of Science ®Google Scholar
• Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH. 1993. The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ. 44(2-3):145–163.
   Web of Science ®Google Scholar
• Lin Y, Zhang L, Wang N, Zhang X, Cen Y, Sun X. 2020. A change detection method using spatial-temporal-spectral information from Landsat images. Int J Remote Sens. 41(2):772–793.
   Web of Science ®Google Scholar
• Liu C, Zhang Q, Luo H, Qi S, Tao S, Xu H, Yao Y. 2019. An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks. Remote Sens Environ. 229(4):114–132.
   Google Scholar
• Liu Q, Liu L, Wang Y. 2017. Unsupervised change detection for multispectral remote sensing images using random walks. Remote Sens. 9(5):438–455.
   Google Scholar
• Lv Z, Liu T, Benediktsson JA. 2020a. Object-oriented key point vector distance for binary land cover change detection using vhr remote sensing images. IEEE Trans Geosci Remote Sens. 58(9):6524–6533.
   Web of Science ®Google Scholar
• Lv Z, Wang FJun, Liu T, Kong XBin, Benediktsson JA. 2020. Local histogram-based analysis for detecting land cover change using VHR remote sensing images. IEEE Geosci Remote Sens Lett. 18(7):1284–1287.
   Web of Science ®Google Scholar
• Lv ZY, Shi WZ, Zhou XC, Benediktsson JA. 2017. Semi-automatic system for land cover change detection using Bi-temporal remote sensing images. Remote Sens. 9(11):1112–1132.
   Google Scholar
• Malila WA. 1980. Change vector analysis: an approach for detecting forest changes with landsat. Proc Soc Photo-Optical Instrum Eng. 326–336.
   Google Scholar
• Mirasi A, Mahmoudi A, Navid H, Valizadeh Kamran K, Asoodar MA. 2021. Evaluation of sum-NDVI values to estimate wheat grain yields using multi-temporal Landsat OLI data. Geocarto Int. 36(12):1309–1324.
   Web of Science ®Google Scholar
• Otsu N. 1979. Threshold selection method from gray-level histograms. IEEE Trans Syst, Man, Cybern. 9(1):62–66.
   Web of Science ®Google Scholar
• Paul A, Chowdary VM, Srivastava YK, Dutta D, Sharma JR. 2017. Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis. Geocarto Int. 32(6):640–654.
   Web of Science ®Google Scholar
• Peng D, Zhang Y. 2017. Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion. Int J Remote Sens. 38(13):3886–3905.
   Web of Science ®Google Scholar
• Petitjean F, Inglada J, Gançarski P. 2012. Satellite image time series analysis under time warping. IEEE Trans Geosci Remote Sens. 50(8):3081–3095.
   Web of Science ®Google Scholar
• Pomente A, Picchiani M, Del Frate F. 2018. Sentinel-2 change detection based on deep features. IGARSS 2018 - 2018 IEEE Int Geosci Remote Sens Symp. 6859–6862.
   Google Scholar
• Saha S, Bovolo F, Bruzzone L. 2019. Unsupervised deep change vector analysis for multiple-change detection in VHR Images. IEEE Trans Geosci Remote Sens. 57(6):3677–3693.
   Web of Science ®Google Scholar
• Saha S, Bovolo F, Bruzzone L, Kessler FB. 2019. Unsupervised multiple-change detection in VHR multisensor images via deep-learning based adaptation. IGARSS 2019 - 2019 IEEE Int Geosci Remote Sens Symp. 5033–5036.
   Google Scholar
• Solano-Correa YT, Bovolo F, Bruzzone L. 2018. An approach for unsupervised change detection in multitemporal VHR images acquired by different multispectral sensors. Remote Sens. 10(4):533–556.
   Google Scholar
• Tang D, Fan H, Zhang Y. 2017. Review on landsat time series change detection methods. J Geo-Inform Sci. 19(8):1069–1079.
   Google Scholar
• Tsui OW, Chiang M, Dean A. 2019. Mapping of bottomfast lake ice in the northwest territories via data mining of synthetic aperture radar image time series. Can J Remote Sens. 45(5):572–590.
   Web of Science ®Google Scholar
• Verbesselt J, Hyndman R, Newnham G, Culvenor D. 2010. Remote sensing of environment detecting trend and seasonal changes in satellite image time series. Remote Sens Environ. 114(1):106–115.
   Web of Science ®Google Scholar
• Verbesselt J, Hyndman R, Zeileis A, Culvenor D. 2010. Remote Sensing of Environment Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ. 114(12):2970–2980.
   Web of Science ®Google Scholar
• Wan L, Xiang Y, You H. 2019. A post-classification comparison method for SAR and optical images change detection. IEEE Geosci Remote Sens Lett. 16(7):1026–1030.
   Web of Science ®Google Scholar
• Wang C, Zhang Y, Wang X. 2019. Coarse-to-fine SAR image change detection method. Remote Sens Lett. 10(12):1153–1162.
   Web of Science ®Google Scholar
• Wu C, Du B, Cui X, Zhang L. 2017. A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion. Remote Sens Environ. 199:241–255.
   Web of Science ®Google Scholar
• Wukelic GE, Gibbons DE, Martucci LM, Foote HP. 1989. Radiometric calibration of landsat thematic mapper thermal band. Remote Sens Environ. 28:339–347.
   Web of Science ®Google Scholar
• Xu H. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). J Remote Sens. 9(5):589–596.
   Google Scholar
• Xu H, Wang Y, Guan H, Shi T, Hu X. 2019. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sens. 11(20):2345.
   Google Scholar
• Yan J, Wang L, Song W, Chen Y, Chen X, Deng Z. 2019. A time-series classification approach based on change detection for rapid land cover mapping. ISPRS J Photogramm Remote Sens. 158(5):249–262.
   Google Scholar
• Yan L, Xia W, Zhao Z, Wang Y. 2018. A novel approach to unsupervised change detection based on hybrid spectral difference. Remote Sens. 10(6):841–862.
   Google Scholar
• Yan WY, van Ewijk K, Treitz P, Shaker A. 2020. Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data. ISPRS J Photogramm Remote Sens. 169(9):152–165.
   Google Scholar
• Yin S, Wu C, Wang Q, Ma W, Zhu L, Yao Y, Wang X, Wu D. 2013. Review of change detection methods using multi-temporal remotely sensed images. Spectrosc Spectr Anal. 33(12):3339–3342.
   Web of Science ®Google Scholar
• Zanetti M, Bovolo F, Bruzzone L. 2015. Rayleigh-rice mixture parameter estimation via em algorithm for change detection in multispectral images. IEEE Trans Image Process. 24(12):5004–5016.
   PubMed Web of Science ®Google Scholar
• Zhang C, Yue P, Tapete D, Jiang L, Shangguan B, Huang L, Liu G. 2020. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS J Photogramm Remote Sens. 166(12):183–200.
   Google Scholar
• Zhang Y, Zhao H. 2020. Land–use and land-cover change detection using dynamic time warping–based time series clustering method. Can J Remote Sens. 46(1):67–83.
   Web of Science ®Google Scholar
• Zhao J, Itti L. 2018. shapeDTW: shape dynamic time warping. Pattern Recognit. 74:171–184.
   Web of Science ®Google Scholar
• Zhu Z. 2017. Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS J Photogramm Remote Sens. 130(10):370–384.
   Google Scholar
• Zhu Z, Woodcock CE. 2014. Continuous change detection and classi fi cation of land cover using all available Landsat data. Remote Sens Environ. 144:152–171.
   Web of Science ®Google Scholar