oalogo2  

AUTHOR(S):

J. Jennifer Ranjani

 

TITLE

A Study on Intelligent Algorithms for Change Detection using Remote Sensing Images

pdf PDF

ABSTRACT

Magnitude of images observed from the remote sensing satellites increases day-by-day and is widely utilized for change detection. Change detection is primitively employed for monitoring the local, global and regional resources, land-cover and land-use monitoring and for environmental studies and disaster management. Remote sensing satellites afford a prospect to obtain the information about the land at varying resolution and time, makes it ideal for change detection studies. A wide variety of algorithms are available for change detection using the remote sensing images and still it is an emerging field. Intelligent algorithms can be employed in supervised, semi-supervised and unsupervised environments. In this paper, we have introduced the drawbacks of the traditional pixel based approaches and the need for intelligent algorithms for change detection. The impact of the latest intelligent change detection algorithms utilizing genetic algorithm, artificial neural network and support vector machine is highlighted. The features of the various intelligent algorithms are summarized and dataset incorporated in the experiments are also indicated. With latest availability of very high resolution images and high computing power, intelligent algorithms are the need of the hour. This paper gives a glimpse on the latest intelligent algorithms available for change detection.

KEYWORDS

Change Detection, Intelligent Algorithms, Artificial Neural Network, Genetic Algorithm, Support Vector Machine, Remote Sensing

REFERENCES

[1] J. Im, J. Rhee, J.R. Jensen, M.E. Hodgson, An automated binary change detection model using a calibration approach, Remote Sensing of Environment 106, 2007, pp. 89–105.

[2] P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin, Digital Change Detection Methods in Ecosystem Monitoring: A Review, International Journal of Remote Sensing 25, 2004, pp. 1565–1596.

[3] J. Im and J.R. Jensen, A Change Detection Model based on Neighborhood Correlation Image Analysis and Decision Tree Classification Remote Sensing of Environment 99, 2005, pp. 326–340.

[4] R.D. Macleod, and R.G. Congalon, A Quantitative Comparison of Changde Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data, Photogrammetric Engineering & Remote Sensing 64, 1998, pp. 207–216.

[5] J. Im, J.R. Jensen, M.E. Hodgson, Optimizing the binary discriminant function in change detection applications, Remote Sensing of Environment 112, 2008, pp. 2761–2776.

[6] J.R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall, Toronto, 2005.

[7] D. Lu, E. Moran, S. Hetrick, and G. Li, Land– use and Land–Cover Change Detection, In: Q. Weng (Ed.), Advances in Environmental Remote Sensing Sensors, Algorithms and Applications, CRC Press Taylor & Francis Group, New York, 2011, pp. 273–290.

[8] N.A. Quarmby, and J.L. Cushnie, Monitoring Urban Land Cover Changes at the Urban Fringe from SPOT HRV Imagery in South–East England, International Journal of Remote Sensing 10, 1989, pp. 953–963.

[9] P.R. Coppin, and M.E. Bauer, Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery, Remote Sensing Reviews 13, 1996, pp. 207-234.

[10] P.J. Howarth, G.M. Wickware, Procedures for Change Detection using Landsat Digital Data, International Journal of Remote Sensing 2, 1981, pp. 277–291.

[11] A.K. Ludeke, R.C. Maggio, and L.M. Reid, An Analysis of Anthropogenic Deforestation using Logistic Regression and GIS, Journal of Environmental Management 31, 1990, pp. 247–259.

[12] A. Singh, Change Detection in the Tropical Forest Environment of North eastern India using Landsat, Remote Sensing and Tropical Land Management, John Wiley, 1986, pp. 237–254.

[13] T.L. Sohl, Change analysis in the United Arab Emirates: an investigation of techniques, Photogrammetric Engineering & Remote Sensing 65, 1999, pp. 475–484.

[14] E.H. Wilson, and S.A. Sader, Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sensing of Environment 80, 2002, pp. 385–396.

[15] R.D. Johnson, and E.S. Kasischke, Change vector analysis: a technique for the multispectral monitoring of land cover and condition, International Journal of Remote Sensing 19, 1998, pp. 411–426.

[16] J. Chen, P. Gong, C. He, R. Pu, and P. Shi, Landuse/ land-cover change detection using improved change-vector analysis, Photogrammetric Engineering & Remote Sensing 69, 2003, pp. 369– 379.

[17] K. Nackaerts, K. Vaesen, B. Muys, and P. Coppin, Comparative performance of a modified change vector analysis in forest change detection, International Journal of Remote Sensing 26, 2005, pp. 839–852.

[18] Y. Bayarjargal, A. Karnieli, M. Bayasgalan, S. Khudulmur, C. Gandush, and C.J. Tucker, A Comparative Study of NOAA–AVHRR Derived Drought Indices using Change Vector Analysis, Remote Sensing of Environment 105, 2006, pp. 9–22.

[19] J.A. Richards, Thematic mapping from multitemporal image data using the principal components transformation, Remote Sensing of Environment 16, 1984, pp. 35–46.

[20] J.S. Deng, K. Wang, Y.H. Deng, and G.J. Qi, PCA-based land-use change detection and analysis using multi-temporal and multi-sensor satellite data, International Journal of Remote Sensing 29, 2008, pp. 4823–838.

[21] S. Jin, and S.A. Sader, Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances, Remote Sensing of Environment 94, 2005, pp. 364–372.

[22] J. Rogan, J. Franklin, and D.A. Roberts, A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery, Remote Sensing of Environment 80, 2002, pp. 143–156.

[23] D. Tomowski, M. Ehlers, and S. Klonus, Colour and Texture Based Change Detection for Urban Disaster Analysis, Urban Remote Sensing Event(JURSE), 2011 Joint, pp. 329–332.

[24] M.A. Wulder, S.M. Ortlepp, J.C. White, N.C. Coops, and S.B. Coggins, Monitoring tree– level insect population dynamics with multi– scale and multi–source remote sensing, Journal of Spatial Science 53, 2008, pp. 49–61.

[25] M.-H. Tseng, S.-J. Chen, G.-H. Hwang, and M.-Y. Shen, A Genetic Algorithm rule based approach for Land–Cover Classification, ISPRS Journal of Photogrammetry and Remote Sensing 63, 2008, pp. 202–212.

[26] T. Celik, Change Detection in Satellite Images using a Genetic Algorithm Approach, IEEE Geoscience and Remote Sensing Letters 7, 2010, pp. 386–390.

[27] T. Celik, Image Change Detection using Gaussian Mixture Model and Genetic Algorithm, Journal of Visual Communication and Image Representation 21, 2010, pp. 965–974.

[28] F. Pacifici, F. D. Frate, C. Solimini, and W. J. Emery, An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery, IEEE Transactions on Geoscience and Remote Sensing 45,2007, pp. 2940–2951.

[29] R. Eckhorn, H. J. Reitboeck, M. Arndt, and P. Dicke, Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex, Neural Comput. 2, 1990, pp. 293–307.

[30] F. Pacifici, and F. D. Frate, Automatic Change Detection in Very-High Resolution Images with Pulse-Coupled Neural Networks, IEEE Geoscience and Remote Sensing Letters 7, 2010, pp. 58–62.

[31] C. Pratola, F. D. Frate, and G. Schiavon, Toward Fully Automatic Detection of Changes in Suburban Areas from VHR SAR Images by Combining Multiple Neural-Network Models, IEEE Transactions on Geoscience and Remote Sensing 51, 2013, pp. 2055–2066.

[32] Y. Zhong,W. Liu, J. Zhao, and L.Zhang, Change Detection based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery, IEEE Geoscience and Remote Sensing Letters 12, 2015, pp. 537–541.

[33] A. Ghosh, B. N. Subudhi, and L. Bruzzone, Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multi-temporal Images, IEEE Transactions on Image Processing 22, 2013, pp. 3087–3096

[34] Q. Wang, W. Shi, P. M. Atkinson, and Z. Li, Land Cover Change Detection at Subpixel Resolution with a Hopfield Neural Network, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, 2015, pp. 1339–1352.

[35] M. Roy, S. Ghosh, and A. Ghosh, A Neural Approach under Active Learning Mode for Change Detection in Remotely Sensed Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 2014, pp. 1200–1206.

[36] V.-E. Neagoe, R.-M. Stoica, A.-I. Ciurea, L. Bruzzone, and F. Bovolo, 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, 2014, pp. 3525-3533.

[37] G. L. Grinblat, L. C. Uzal, and P. M. Granitto, Abrupt Change Detection with One-class Timeadaptive Support Vector Machines, Expert Systems with Applications 40, 2013, pp. 7242–7249.

[38] H. Hichri, Y. Bazi, N. Alajlan, and S. Malek, Interactive Segmentation for Change Detection in Multispectral Remote -Sensing Images, IEEE Geoscience and Remote Sensing Letters 10, 2013, pp. 298–302.

[39] F. D. Morsier, D. Tuia, M. Boregeaud, V. Gass, J.-P. Thiran, Semi-supervised Novelty Detection using SVM Entire Solution, IEEE Geoscience and Remote Sensing 51, 2013, pp. 1939–1950.

[40] L. Jia, M. Li, Y. Wu, P. Zhang, H. Chen, and L. An, Semisupervised SAR Image Change Detection using a Cluster-Neighborhood Kernel, IEEE Geoscience and Remote Sensing Letters 11, 2014, pp. 1443–1447.

[41] H. Li, M. Li, P. Zhang, W. Song, L. An, and Y. Wu, SAR Image Change Detection based on Hybrid Conditional Random Field, IEEE Geoscience and Remote Sensing Letters 12, 2015, pp. 910-914.

[42] L. Jia, M. Li, Y. Wu, P. Zhang, G. Liu, H. Chen, and L. An, Change Detection based on Iterative Label-Information Composite Kernel Supervised by Anisotrophic Texture, IEEE Transaction on Geoscience and Remote Sensing 53, 2015, pp. 3960–3973.

Cite this paper

J. Jennifer Ranjani. (2017) A Study on Intelligent Algorithms for Change Detection using Remote Sensing Images. International Journal of Signal Processing, 2, 133-139

 

cc.png
Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0