Abstract
In this paper, I propose a new unsupervised change detection method for optical satellite imagery. The proposed technique consists of three phases. In the first stage, difference images are calculated using four different functions. Two of the functions were first used in this study. In the second stage, using Reconstruction Independent Component Analysis, this four-difference matrix is projected to one feature. In the last stage, clustering is performed. Kmeans tuned by Artificial Bee Colony (ABC-Kmeans) clustering technique has been developed and proposed by following a different strategy in the clustering phase. The effectiveness of the proposed approach was examined using two different datasets, Sardinia and Mexico. Quantitative evaluation was performed in two stages. In the first stage, proposed method was compared with different unsupervised change detection algorithms using False Alarm, Missed Alarm, Total Error, and Total Error Rate metrics which are calculated using ground truth image in dataset. In the second experimental study, the proposed approach is compared in detail with PCA-Kmeans approach, which is quite often preferred for similar studies, using the Mean Squared Error, Peak Signal to Noise Ratio, Structural Similarity Index, and Universal Image Quality Index metrics. According to quantitative and qualitative analysis, proposed approach can produce quite successful results using optical remote sensing data.
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Agrawal, K. P., Garg, S., Sharma, S., & Patel, P. (2016). Development and validation of OPTICS based spatio-temporal clustering technique. Information Sciences, 369, 388–401. https://doi.org/10.1016/j.ins.2016.06.048.
Andrade, G., Ramos, G., Madeira, D., Sachetto, R., Ferreira, R., & Rocha, L. (2013). G-DBSCAN: A GPU accelerated algorithm for density-based clustering. Procedia Computer Science, 18, 369–378. https://doi.org/10.1016/j.procs.2013.05.200.
Atasever, U. H., Civicioglu, P., Besdok, E., & Ozkan, C. (2014). A new unsupervised change detection approach based on DWT image fusion and backtracking search optimization algorithm for optical remote sensing data. Int Arch Photogramm Remote Sens Spatial Inf Sci, XL-7, 15–18. https://doi.org/10.5194/isprsarchives-XL-7-15-2014.
Atasever, U. H., Kesikoglu, M. H., & Ozkan, C. (2016). A new artificial intelligence optimization method for PCA based unsupervised change detection of remote sensing image data. Neural Network World, 26, 141–154. https://doi.org/10.14311/nnw.2016.26.008.
Barekatain, B., Dehghani, S., & Pourzaferani, M. (2015). An energy-aware routing protocol for wireless sensor networks based on new combination of genetic algorithm & k-means. Procedia Computer Science, 72, 552–560. https://doi.org/10.1016/j.procs.2015.12.163.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM - the fuzzy C-means clustering algorithm. Comput Geosci-Uk, 10, 191–203. https://doi.org/10.1016/0098-3004(84)90020-7.
Celik, T. (2009a). Multiscale change detection in multitemporal satellite images. IEEE Geoscience and Remote Sensing Letters, 6, 820–824. https://doi.org/10.1109/LGRS.2009.2026188.
Celik, T. (2009b). Unsupervised change detection in satellite images using principal component analysis and K-means clustering. IEEE Geoscience and Remote Sensing Letters, 6, 772–776. https://doi.org/10.1109/LGRS.2009.2025059.
Celik, T. (2010). Change detection in satellite images using a genetic algorithm approach. IEEE Geoscience and Remote Sensing Letters, 7, 386–390. https://doi.org/10.1109/LGRS.2009.2037024.
Civicioglu, P., & Besdok, E. (2013). A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39, 315–346. https://doi.org/10.1007/s10462-011-9276-0.
Diwold, K., Aderhold, A., Scheidler, A., & Middendorf, M. (2011). Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Computing, 3, 149–162. https://doi.org/10.1007/s12293-011-0065-8.
Do, C. M., & Javidi, B. (2007). Multifocus holographic 3-D image fusion using independent component analysis. Journal of Display Technology, 3, 326–332. https://doi.org/10.1109/JDT.2007.900918.
Dou, M., Chen, J., Chen, D., Chen, X., Deng, Z., Zhang, X., Xu, K., & Wang, J. (2014). Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images. Future Generation Computer Systems, 37, 367–377. https://doi.org/10.1016/j.future.2013.12.018.
El-Asmar, H. M., & Hereher, M. E. (2011). Change detection of the coastal zone east of the Nile Delta using remote sensing. Environmental Earth Sciences, 62, 769–777. https://doi.org/10.1007/s12665-010-0564-9.
Erbek, F. S., Özkan, C., & Taberner, M. (2004). Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing, 25, 1733–1748. https://doi.org/10.1080/0143116031000150077.
Gao, W. F., Huang, L. L., Liu, S. Y., & Dai, C. (2015). Artificial bee colony algorithm based on information learning. IEEE Transactions on Cybernetics, 45, 2827–2839. https://doi.org/10.1109/TCYB.2014.2387067.
Ghosh, A., Mishra, N. S., & Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181, 699–715. https://doi.org/10.1016/j.ins.2010.10.016.
Gong, M., Zhou, Z., & Ma, J. (2012). Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Transactions on Image Processing, 21, 2141–2151. https://doi.org/10.1109/TIP.2011.2170702.
Gunen, M. A., Civicioglu, P., & Beşdok, E. (2016). Differential search algorithm based edge detection. Int Arch Photogramm Remote Sens Spatial Inf Sci, XLI-B7, 667–670. https://doi.org/10.5194/isprs-archives-XLI-B7-667-2016.
Hao, M., Zhang, H., Shi, W., & Deng, K. (2013). Unsupervised change detection using fuzzy C-means and MRF from remotely sensed images. Remote Sensing Letters, 4, 1185–1194. https://doi.org/10.1080/2150704X.2013.858841.
Hore, A., & Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. In 2010 20th International Conference on Pattern Recognition, 23–26 Aug (Vol. 2010, pp. 2366–2369). https://doi.org/10.1109/ICPR.2010.579.
Ji, J., Pang, W., Zheng, Y., Wang, Z., & Ma, Z. (2015). A novel artificial bee colony based clustering algorithm for categorical data. PLoS One, 10, e0127125. https://doi.org/10.1371/journal.pone.0127125.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471. https://doi.org/10.1007/s10898-007-9149-x.
Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11, 652–657. https://doi.org/10.1016/j.asoc.2009.12.025.
Krinidis, S., & Chatzis, V. (2010). A robust fuzzy local information C-means clustering algorithm. IEEE Transactions on Image Processing, 19, 1328–1337. https://doi.org/10.1109/TIP.2010.2040763.
Kurban, T., Civicioglu, P., Kurban, R., & Besdok, E. (2014). Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl Soft Comput, 23, 128–143. https://doi.org/10.1016/j.asoc.2014.05.037.
Le, Q. V., Karpenko, A., Ngiam, J., Ng, A. Y. (2011). ICA with reconstruction cost for efficient overcomplete feature learning. Paper presented at the Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain.
Lei, Y., Shan, H., Jia, F., Lin, J. (2016). Reconstruction independent component analysis-based methods for intelligent fault diagnosis. In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 4–6 May 2016 (pp 245–250). https://doi.org/10.1109/CSCWD.2016.7565996.
Ma, W., Jiao, L., Gong, M., & Li, C. (2013). Image change detection based on an improved rough fuzzy C-means clustering algorithm. International Journal of Machine Learning and Cybernetics, 5, 369–377. https://doi.org/10.1007/s13042-013-0174-4.
Mahesh Kumar, K., & Rama Mohan Reddy, A. (2016). A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method. Pattern Recognition, 58, 39–48. https://doi.org/10.1016/j.patcog.2016.03.008.
Mishra, N. S., Ghosh, S., & Ghosh, A. (2012). Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Applied Soft Computing, 12, 2683–2692. https://doi.org/10.1016/j.asoc.2012.03.060.
Neagoe, V. E., Chirila-Berbentea, V. (2016). Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 10–15 July 2016 (pp 3063–3065). https://doi.org/10.1109/IGARSS.2016.7729792.
Neagoe, V. E., Stoica, R. M., Ciurea, A. I., Bruzzone, L., & Bovolo, F. (2014). Concurrent self-organizing maps for supervised/unsupervised change detection in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3525–3533. https://doi.org/10.1109/JSTARS.2014.2330808.
Ntajal, J., Lamptey, B. L., Mahamadou, I. B., & Nyarko, B. K. (2017). Flood disaster risk mapping in the Lower Mono River Basin in Togo, West Africa. International Journal of Disaster Risk Reduction, 23, 93–103. https://doi.org/10.1016/j.ijdrr.2017.03.015.
Ozturk, C., Karaboga, D., & Gorkemli, B. (2011). Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm. Sensors (Basel), 11, 6056–6065. https://doi.org/10.3390/s110606056.
Patra, S., Ghosh, S., Ghosh, A. (2007). Unsupervised change detection in remote-sensing images using modified self-organizing feature map neural network. In: Computing: Theory and applications, 2007. ICCTA '07. International conference on, 5–7 March 2007 (pp 716-720). https://doi.org/10.1109/ICCTA.2007.128.
Rahman, M. A., & Islam, M. Z. (2014). A hybrid clustering technique combining a novel genetic algorithm with K-means. Knowledge-Based Systems, 71, 345–365. https://doi.org/10.1016/j.knosys.2014.08.011.
Serapião, A. B. S., Corrêa, G. S., Gonçalves, F. B., & Carvalho, V. O. (2016). Combining K-means and K-harmonic with fish school search algorithm for data clustering task on graphics processing units. Applied Soft Computing, 41, 290–304. https://doi.org/10.1016/j.asoc.2015.12.032.
Subudhi, B. N., Bovolo, F., Ghosh, A., & Bruzzone, L. (2014). Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images. Optics & Laser Technology, 57, 284–292. https://doi.org/10.1016/j.optlastec.2013.10.003.
Unsalan, C., & Boyer, K. L. (2004). Linearized vegetation indices based on a formal statistical framework. IEEE Transactions on Geoscience and Remote Sensing, 42, 1575–1585. https://doi.org/10.1109/TGRS.2004.826787.
Zhang, W., Wang, W., & Wu, F. (2012). The application of multi-variable optimum regression analysis to remote sensing imageries in monitoring landslide disaster energy. Procedia, 16(Part A), 190–196. https://doi.org/10.1016/j.egypro.2012.01.032.
Zhao, B., Zhong, Y., Ma, A., & Zhang, L. (2016). A spatial Gaussian mixture model for optical remote sensing image clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 5748–5759. https://doi.org/10.1109/JSTARS.2016.2546918.
Zheng, Y., Zhang, X., Hou, B., & Liu, G. (2014). Using combined difference image and K-means clustering for SAR image change detection. IEEE Geoscience and Remote Sensing Letters, 11, 691–695. https://doi.org/10.1109/LGRS.2013.2275738.
Zhong, Y., Ma, A., Ong, Y., Zhu, Z., & Zhang, L. (2018). Computational intelligence in optical remote sensing image processing. Applied Soft Computing, 64, 75–93. https://doi.org/10.1016/j.asoc.2017.11.045.
Zhou, W., & Bovik, A. C. (2002). A Universal Image Quality Index. IEEE Signal Processing Letters, 9, 81–84. https://doi.org/10.1109/97.995823.
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Atasever, U.H. A novel unsupervised change detection approach based on reconstruction independent component analysis and ABC-Kmeans clustering for environmental monitoring. Environ Monit Assess 191, 447 (2019). https://doi.org/10.1007/s10661-019-7591-0
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DOI: https://doi.org/10.1007/s10661-019-7591-0