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Evaluating the performance of change indices extracted from multi-temporal remote sensing images in detecting land use and land cover changes
Younes Naeimi *, Ramin Norouzi, Vahid Sadeghi
Abstract:   (30 Views)
In this paper, the performance of 8 change indices including Euclidean Distance (ED), Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), image regression, ERGAS, spectral-spatial correlation, mutual information (MI), and Jeffries-Matusita Distance (JMD) has been compared on two different datasets from the accuracy and computational time points of view. The first dataset includes a pair of bi-temporal images taken by Landsat TM5 and ETM+ sensors over the southern shores of Lake Urmia, and the second dataset is taken by Landsat TM4 and TM5 sensors overs the Maragheh city and sorrounding area. Implementing the mentioned indices on the first dataset indicates the SAM's significant superiority compared to the other indices. False alarm (FA), missed error (ME), and total error (TE) of the change map resulting from SAM are 3.40%, 13.91%, and 8.86%, respectively. The change map resulting from SCM is in the second order, its FA, ME, and TE values are almost twice the corresponding values derived from SAM. JMD, regression, MI, ED, and ERGAS indices were in the next ranks respectively with 20.17%, 20.61%, 20.84%, 21.22%, and 21.47% TE on their change maps. In the first dataset, the worst change detection result was obtained from the correlation index (TE=27.80%). In the second dataset, the best results have been obtained first from the ERGAS and then from the magnitude of change, which were at the top compared to others. The FA, ME, and TE values of the change map resulting from the ERGAS were 0.63%, 26.54%, and 7.5%, respectively, and the FA, ME, and TE values of the change map resulting from the ED were 0.63%, 32.23%, and 9.01%, respectively. In the lower ranks, SCM, SAM, regression, spectral-spatial correlation, and JMD have led to 11.41%, 12.62%, 14.45%, 17.34%, and 18.03% total errors in change detection, respectively. The worst result with 26.56% TE was achieved from the MI. It is noted should be that the significance of the differences in accuracy between the mentioned indices was tested and verified by McNemar’s test. In terms of computation time, the ED was the most efficient, while the MI was time-consuming on the analysed datasets.
Article number: 9
Keywords: LULC, change detection, remotely sensed images, change index
Type of Study: Research | Subject: Photo&RS
1. Ebadi, H., Sadeghi, V., Farnood Ahmadi, F. (2020). "Change Detection in Multi-temporal Remote Sensing Images" KNT University Press. Tehran, (In Persian).
2. Sadeghi, V. (2019). "Combining of magnitude and direction of change indices to unsupervised change detection in multitemporal multispectral remote sensing images" Journal of Geomatics Science And Technology. 8 (4): 91-108, (In Persian).
3. Mohsenifar A, Mohammadzadeh A, Moghimi A. (2021). "An Integrated Unsupervised Change Detection Method Based on the Discrete Wavelet Transform Fusion and An Improved Markov Random Field Model" Journal of Geomatics Science And Technology. 10 (3): 165-182, (In Persian).
4. Carvalho Júnior, O.A., et al. (2011). "A new approach to change vector analysis using distance and similarity measures" Remote Sensing. 3(11): 2473-2493. [DOI:10.3390/rs3112473]
5. Mohsenifar, A., Mohammadzadeh, A., Moghimi, A., Salehi, B. (2021) "A novel unsupervised forest change detection method based on the integration of a multiresolution singular value decomposition fusion and an edge-aware Markov Random Field algorithm" International Journal of Remote Sensing. 42(24): 9376-9404. [DOI:10.1080/01431161.2021.1995075]
6. Khankeshizadeh, E., Mohammadzadeh, A., Moghimi, A., Mohsenifar, A. (2022) "FCD-R2U-net: Forest change detection in bi-temporal satellite images using the recurrent residual-based U-net" Earth Science Informatics. 15, 2335-2347. [DOI:10.1007/s12145-022-00885-6]
7. Ramos, J.F., D. Renza, and D.M. Ballesteros L. (2018). "Evaluation of spectral similarity indices in unsupervised change detection approaches" Dyna. 85(204): 117-126. [DOI:10.15446/dyna.v85n204.68355]
8. Singh, A. and Singh, K.K. (2018). "Unsupervised change detection in remote sensing images using fusion of spectral and statistical indices" The Egyptian Journal of Remote Sensing and Space Science. (3): 345-351. [DOI:10.1016/j.ejrs.2018.01.006]
9. Jabari, S., et al. (2019). "Multispectral change detection using multivariate Kullback-Leibler distance" ISPRS Journal of Photogrammetry and Remote sensing. 147: 163-177. [DOI:10.1016/j.isprsjprs.2018.11.014]
10. Sadeghi, V., Ebadi, H., Mohammadzadeh, A. Farnood Ahmadi, F. (2016). "Change detection in multitemporal remote sensing imagery with thresholding of PSO-based fused change index" Journal of Geomatics Science And Technology. 5 (3):175-192, (In Persian).
11. Carvalho Jr, O. and Menezes, P. (2000). "Spectral correlation mapper (SCM): An improving spectral angle mapper". in Ninth JPL Airborne Earth Science Workshop. Pasadena: JPL Publication.
12. Hussain, M., et al. (2013). "Change detection from remotely sensed images: From pixel-based to object-based approaches" ISPRS Journal of Photogrammetry and Remote Sensing. 80: 91-106. [DOI:10.1016/j.isprsjprs.2013.03.006]
13. Coppin, P.R. and Bauer M.E. (1994). "Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features" IEEE Transactions on Geoscience and Remote Sensing. 32(4): 918-927. [DOI:10.1109/36.298020]
14. Wald, L. (2002). "Data fusion: definitions and architectures: fusion of images of different spatial resolutions" Presses des MINES.
15. Renza, D., Martinez, E. and Arquero, A. (2012). "A new approach to change detection in multispectral images by means of ERGAS index" IEEE Geoscience and Remote Sensing Letters. 10(1): 76-80. [DOI:10.1109/LGRS.2012.2193372]
16. Yang, Z. and Mueller, R (2007). "Spatial-spectral cross-correlation for change detection- A case study for Citrus coverage change detection" ASPRS 2007 Annual conference. Citeseer.
17. Yasuoka, Y., et al. (1988). "Land-cover change from remotely sensed images using spectral signature similarity" 9th Asian Conference on Remote Sensing. Bangkok, Thailand.
18. Shannon, C.E. (1948). "A mathematical theory of communication" The Bell system technical journal. 27(3): 379-423. [DOI:10.1002/j.1538-7305.1948.tb01338.x]
19. Hossain, M.A., Jia, X., and Pickering, M. (2013). "Subspace detection using a mutual information measure for hyperspectral image classification" IEEE Geoscience and Remote Sensing Letters. 11(2): 424-428. [DOI:10.1109/LGRS.2013.2264471]
20. Jafarzadeh, H. and Hasanlou, M. (2019). "Probability estimation of change maps using spectral similarity" Multidisciplinary Digital Publishing Institute Proceedings. [DOI:10.3390/ECRS-3-06183]
21. Nussbaum, S., Niemeyer, I., and Canty, M. (2006). "SEATH-a new tool for automated feature extraction in the context of object-based image analysis" 1st International Conference on Object-based Image Analysis (OBIA). Salzburg: Austria.
22. Mhangara, P. and Odindi, J. (2013). "Potential of texture-based classification in urban landscapes using multispectral aerial photos" South African Journal of Science. 109(3): 1-8. [DOI:10.1590/sajs.2013/1273]
23. Sadeghi, V., Ebadi, H., Farnood Ahmadi, F.( 2013)."Automatic Normalization of Multi-temporal Satellite Images using Artificial Neural Network and mathematical methods" Applied Mathematical Modelling. 37(9):6437-6445. [DOI:10.1016/j.apm.2013.01.006]
24. Sadeghi, V., Farnood Ahmadi, F., and Ebadi, H. (2015). "A new automatic regression-based approach for relative radiometric normalization of multitemporal satellite imagery" Computational and Applied Mathematics: 1-18. [DOI:10.1007/s40314-015-0254-z]
25. Kiani, A., Farnood Ahmadi, F., Ebadi, H. (2020). "Developing an Interpretation System for High-Resolution Remotely Sensed Images Based on Hybrid Decision-Making Process in a Multi-scale Manner" Journal of the Indian Society of Remote Sensing: 48, 197-214. [DOI:10.1007/s12524-019-01069-4]
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نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology