Skip to main content

Advertisement

SpringerLink
Log in

New framework for hyperspectral change detection based on multi-level spectral unmixing

  • Original Paper
  • Published:
Applied Geomatics Aims and scope Submit manuscript

Abstract

Earth is constantly changing due to some natural events and human activities that threaten our environment. Thus, accurate and timely monitoring of these changes is of great importance for properly coping with their consequences. In this regard, this research presented a new framework for hyperspectral change detection (HCD) based on dynamic time warping (DTW) and multi-level spectral unmixing. The proposed method included two parts. The first part provided the binary change map based on Otsu and DTW algorithms. The DTW algorithm plays the role of a robust predictor for HCD purposes and the Otsu algorithm selects the threshold for detecting change and no-change areas. The second part presented a multiple change map based on the local spectral unmixing procedure and the output of the image differencing (ID) algorithm. The second part, at the first step, uses the ID to predict change and no-change areas and then employs the binary change map for mask no-change pixels. The endmember estimation and extraction was applied to change pixels, and the correlation coefficient among the bands was calculated simultaneously. Next, change pixels were divided into many parts based on the correlation among the bands. In addition, the abundance map was estimated, and then the labeling process was applied for each part. Finally, the multiple change map was obtained by the fusion of the labels of all parts. The result of HCD was compared to those of other robust HCD methods by two real bi-temporal hyperspectral datasets. Based on the result of HCD in binary and multiple change maps, the proposed method had high performance compared to other methods and its overall accuracy and kappa coefficient were more than 90% and 0.77, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. Minimum Volume Constrained Nonnegative Matrix Factorization.

  2. Convex Analysis–Based Minimum-Volume Enclosing Simplex.

  3. Non-Negatively Constrained Least Squares.

References

  • Ambikapathi A, Chan T-H, Chi C-Y, Keizer K (2012) Hyperspectral data geometry-based estimation of number of endmembers using p-norm-based pure pixel identification algorithm. IEEE Trans Geosci Remote Sens 51(5):2753–2769

    Article  Google Scholar 

  • Bioucas-Dias JM, Plaza A, Dobigeon N, Parente M, Du Q, Gader P, Chanussot J (2012) Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth Observ Remote Sens 5(2):354–379

    Article  Google Scholar 

  • Chan T-H, Chi C-Y, Huang Y-M, Ma W-K (2009) A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. IEEE Trans Signal Process 57(11):4418–4432

    Article  Google Scholar 

  • Chan T-H, Ma W-K, Ambikapathi A, Chi C-Y (2011) A simplex volume maximization framework for hyperspectral endmember extraction. IEEE Trans Geosci Remote Sens 49(11):4177–4193

    Article  Google Scholar 

  • Cheng K, Wang J (2019) Forest-type classification using time-weighted dynamic time warping analysis in mountain areas: a case study in Southern China. Forests 10(11):1040

    Article  Google Scholar 

  • Csillik O, Belgiu M, Asner GP, Kelly M (2019) Object-based time-constrained dynamic time warping classification of crops using Sentinel-2. Remote Sens 11(10):1257

    Article  Google Scholar 

  • Datt B, McVicar TR, Van Niel TG, Jupp DL, Pearlman JS (2003) Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Trans Geosci Remote Sens 41(6):1246–1259

    Article  Google Scholar 

  • Deng J, Wang K, Deng Y, Qi G (2008) PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int J Remote Sens 29(16):4823–4838

    Article  Google Scholar 

  • Ertürk A, Iordache M-D, Plaza A (2016) Sparse unmixing with dictionary pruning for hyperspectral change detection. IEEE J Sel Top Appl Earth Observ Remote Sens 10(1):321–330

    Article  Google Scholar 

  • Ghosh D, Chakravortty S (2020) Change detection of tropical mangrove ecosystem with subpixel classification of time series hyperspectral imagery. In: Artificial intelligence techniques for satellite image analysis. Springer, pp 189–211

  • Han Y, Chang A, Choi S, Park H, Choi J (2017) An unsupervised algorithm for change detection in hyperspectral remote sensing data using synthetically fused images and derivative spectral profiles. J Sensors 2017

  • Hasanlou M, Seydi ST (2018) Hyperspectral change detection: an experimental comparative study. Int J Remote Sens 39(20):7029–7083

    Article  Google Scholar 

  • Hasanlou M, Seydi ST, Shah-Hosseini R (2018) A sub-pixel multiple change detection approach for hyperspectral imagery. Can J Remote Sens 44(6):601–615

    Article  Google Scholar 

  • Huang F, Yu Y, Feng T (2019) Hyperspectral remote sensing image change detection based on tensor and deep learning. J vis Commun Image Represent 58:233–244

    Article  Google Scholar 

  • Jianzhuang L, Wenqing L, Yupeng T Automatic thresholding of gray-level pictures using two-dimension Otsu method. In: China., 1991 International Conference on Circuits and Systems, 1991. IEEE, pp 325–327

  • Jin Q, Ma Y, Pan E, Fan F, Huang J, Li H, Sui C, Mei X (2019) Hyperspectral unmixing with gaussian mixture model and spatial group sparsity. Remote Sens 11(20):2434

    Article  Google Scholar 

  • Khurshid KS, Staenz K, Sun L, Neville R, White HP, Bannari A, Champagne CM, Hitchcock R (2006) Preprocessing of EO-1 Hyperion data. Can J Remote Sens 32(2):84–97

    Article  Google Scholar 

  • Kwan C, Ayhan B, Chen G, Wang J, Ji B, Chang C-I (2006) A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents. IEEE Trans Geosci Remote Sens 44(2):409–419

    Article  Google Scholar 

  • Liu S (2015) Advanced techniques for automatic change detection in multitemporal hyperspectral images. University of Trento

  • Liu S, Bruzzone L, Bovolo F, Du P (2016) Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images. IEEE Trans Geosci Remote Sens 54(5):2733–2748

    Article  Google Scholar 

  • Liu S, Du Q, Tong X, Samat A, Bruzzone L, Bovolo F (2017) Multiscale morphological compressed change vector analysis for unsupervised multiple change detection. IEEE J Sel Top Appl Earth Observ Remote Sens 10(9):4124–4137

    Article  Google Scholar 

  • López-Fandiño J, Heras DB, Argüello F, Dalla Mura M (2019) GPU framework for change detection in multitemporal hyperspectral images. Int J Parallel Prog 47(2):272–292

    Article  Google Scholar 

  • Marinelli D, Bovolo F, Bruzzone L (2019) A novel change detection method for multitemporal hyperspectral images based on binary hyperspectral change vectors. IEEE Trans Geosci Remote Sens 57(7):4913–4928

    Article  Google Scholar 

  • Miao L, Qi H (2007) Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Trans Geosci Remote Sens 45(3):765–777

    Article  Google Scholar 

  • Rashwan S, Dobigeon N, Sheta W, Hassan H (2019) Non-linear unmixing of hyperspectral images using multiple-kernel self-organising maps. IET Image Process 13(12):2190–2195

    Article  Google Scholar 

  • Seydi ST, Hasanlou M (2017) A new land-cover match-based change detection for hyperspectral imagery. Eur J Remote Sens 50(1):517–533

    Article  Google Scholar 

  • Song A, Choi J, Han Y, Kim Y (2018) Change detection in hyperspectral images using recurrent 3D fully convolutional networks. Remote Sens 10(11):1827

    Article  Google Scholar 

  • Sun F, Gu Y, Cao Y, Lu Q, Bai Y, Li L, Hao M, Qu C, Wang S, Liu L (2019) Novel flexible pressure sensor combining with dynamic-time-warping algorithm for handwriting identification. Sens Actuators A Phys 293:70–76

    Article  Google Scholar 

  • Vala HJ, Baxi A (2013) A review on Otsu image segmentation algorithm. Int J Adv Res Comput Eng Technol (IJARCET) 2(2):387–389

    Google Scholar 

  • Wang M, Zhao M, Chen J, Rahardja S (2019a) Nonlinear unmixing of hyperspectral data via deep autoencoder networks. IEEE Geosci Remote Sens Lett 16(9):1467–1471

    Article  Google Scholar 

  • Wang Q, Yuan Z, Du Q, Li X (2019b) GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Trans Geosci Remote Sens 57(1):3–13. https://doi.org/10.1109/TGRS.2018.2849692

    Article  Google Scholar 

  • Weisstein EW (2006) Correlation coefficient

  • Wu C, Du B, Zhang L (2013a) Slow feature analysis for change detection in multispectral imagery. IEEE Trans Geosci Remote Sens 52(5):2858–2874

    Article  Google Scholar 

  • Wu C, Du B, Zhang L (2013b) A subspace-based change detection method for hyperspectral images. IEEE J Sel Top Appl Earth Observ Remote Sens 6(2):815–830

    Article  Google Scholar 

  • Xu X, Xu S, Jin L, Song E (2011) Characteristic analysis of Otsu threshold and its applications. Pattern Recog Lett 32(7):956–961

    Article  Google Scholar 

  • Yuan Y, Lv H, Lu X (2015) Semi-supervised change detection method for multi-temporal hyperspectral images. Neurocomputing 148:363–375

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Seyd Teymoor Seydi, Reza Shah-Hosseini & Mahdi Hasanlou

Authors
  1. Seyd Teymoor Seydi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Shah-Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahdi Hasanlou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Shah-Hosseini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Seydi, S.T., Shah-Hosseini, R. & Hasanlou, M. New framework for hyperspectral change detection based on multi-level spectral unmixing. Appl Geomat 13, 763–780 (2021). https://doi.org/10.1007/s12518-021-00385-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12518-021-00385-0

Keywords

Search

Navigation