A Review on Segmentation Techniques in Large-Scale Remote Sensing Images


  • Rakesh Tripathi M.Tech Scholar TIEIT, Bhopal, M.P, India
  • Neelesh Gupta Professor, TIEIT, Bhopal, M.P, India




Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.


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Gonzalez, R.C., Woods, R.E.: Digital Image Processing. 2nd edn. Pearson Education, Inc., Singapore, 2002.

Qizhi Xu, Bo Li, Zhaofeng He, and Chao M “Multiscale Contour Extraction Using a Level Set Method in Optical Satellite Images”, IEEE Geoscience and Remote Sensing letters, Vol 8,No.5, Sept-2011.

Taxt, T., Flynn, P.J., Jain, A.K., “Segmentation of document images”, IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 11, pp. 1322–1329, 1989.

Nakagawa, Y., A.Rosenfeld, “Some experiments on variable thresholding”, Pattern Recognition, Vol. 11, pp. 191–204, 1979.

Yanowitz, S.D., Bruckstein, A.M., “A new method for image segmentation”, Computer Vision, Graphics and Image Processing, Vol. 46, pp. 82–95, 1989.

Kittler, J., Illingworth, J., “Minimum error thresholding. Pattern Recognition”, Vol. 19, pp. 41–47, 1986.

Pal, N.R., Bhandari, D., “On object-background classification”, Int. J. Systems Science Vol. 23, pp. 1903–1920, 1992.

Geman, S., Geman, D., “Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images”, IEEE Trans. Pattern Analysis and Machine Intelligence Vol 6, pp. 707–720, 1984.

Babaguchi, N., Yamada, K., Kise, K., Tezuka, T., “Connectionist model binarization”, In: Proc. 10th ICPR, pp. 51–56, 1990.

Blanz, W.E., Gish, S.L., “A connectionist classifier architecture applied to image segmentation”, In: Proc. 10th ICPR., pp. 272–277, 1990.

Chen, C.T., Tsao, E.C., Lin, W.C., “Medical image segmentation by a constraint satisfaction neural network”, IEEE Trans. Nuclear Science, Vol. 38, pp. 678–686, 1991.

Ghosh, A., Pal, N.R., Pal, S.K., “Image segmentation using neural networks”, Biological Cybernetics Vol. 66, pp. 151–158, 1991.

Ghosh, A., Pal, N.R., Pal, S.K., “Neural network, Gibbs distribution and object extraction”, EDS.: Intelligent Robotics, pp. 95–106, 1991.

Ghosh, A., Pal, N.R., Pal, S.K., “Object background classification using Hopfield type neural network”, Int. J. Pattern Recognition and Artificial Intelligence, Vol 6, pp. 989–1008, 1992.

Kuntimad, G., Ranganath, H.S., “Perfect image segmentation using pulse coupled neural networks”, IEEE Trans. Neural Networks, Vol. 10, 591–598, 1999.

Ghosh, S., Ghosh, A., “A GA-FUZZY approach to evolve hopfield type optimum networks for object extraction” Int. Conf. on Fuzzy Systems. Volume LNCS-2275., Springer, pp. 444–449, 2003.

Jiang, Y., Zhou, Z., “SOM ensemble-based image segmentation”, Neural Processing Letters Vol. 20, pp. 171–178, 2004.

Pal, S.K., Rosenfeld, A.: Image enhancement and thresholding by optimization of fuzzy compactness. Pattern Recognition Letters Vol. 7, pp. 77–86, 1988.

Hall, L.O., Bensaid, A.M., Clarke, L.P., Velthuizen, R.P., Silbiger, M., Bezdek, J.C., “A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain”, IEEE Trans. Neural Networks Vol. 3, pp. 672–681, 1992.

Bhanu, B., Fonder, S., “Functional template-based SAR image segmentation”, Pattern Recognition, Vol. 37, pp. 61–77, 2004.

Davis, L.S., “A survey of edge detection techniques”, Computer Graphics and Image Processing, Vol. 4, pp. 248–270, 1975.

Acharyya, M., De, R.K., Kundu, M.K., “Segmentation of remotely sensed images using wavelets features and their evaluation in soft computing framework”, IEEE Trans. Geoscience and Remote Sensing Vol. 41, pp. 2900–2905, 2003.

Cheng, H.D., Jiang, X.H., Sun, Y.,Wang, J. “Color image segmentation: Advances and prospects”, Pattern Recognition Vol. 34, pp. 2259–2281, 2001.

Naik, S.K., Murthy, C., “A.: Standardization of edge magnitude in color images”, IEEE Trans. Image Processing Communicated, 2005.

Naik, S.K., Murthy, C., “Distinct multi-colored region descriptors for object recognition”, IEEE Trans. Pattern Analysis and Machine Intelligence Communicated, 2005.

Bharathi S, P Deepa Shenoy, Shreyas V J , Anirudh R P , Sanketh S M, Venugopal K R, L M Patnaik, “Performance Analysis of Segmentation Techniques for Land Cover Types using Remote sensing images”, IEEE, pp. 775-780, 2012.

Long Ma, Bin Du, He Chen, and Nouman Q. Soomro, “Region-of-Interest Detection via Superpixel-to-Pixel Saliency Analysis for Remote Sensing Image”, IEEE Geoscience and Remote Sensing Letters, pp. 1-5, 2016.

Tong Li, Junping Zhang, Xiaochen Lu and Ye Zhang, “SDBD: A Hierarchical Region-of-Interest Detection Approach in Large-Scale Remote Sensing Image”, IEEE Geoscience and Remote Sensing Letters, pp. 1-5, 2017.




How to Cite

Tripathi, R., & Gupta, N. (2018). A Review on Segmentation Techniques in Large-Scale Remote Sensing Images. SMART MOVES JOURNAL IJOSCIENCE, 4(4), 31–36. https://doi.org/10.24113/ijoscience.v4i4.143