Abstract
This paper introduces a remote sensing image segmentation approach by using semi-supervised and dynamic region merging. In remote sensing images, the spatial relationship among pixels has been shown to be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Through a graph-based technique, unlabeled samples are actively selected based on the entropy of the corresponding class label. With an initially segmented image based semi-supervised, in which the many regions to be merged for a meaningful segmentation. By taking the region merging as a labeling problem, image segmentation is performed by iteratively merging the regions according to a statistical test. Experiments on two datasets are used to evaluate the performance of the proposed method. Comparisons with the state-of-the-art methods demonstrate that the proposed method can effectively investigate the spatial relationship among pixels and achieve better remote sensing image segmentation results.
Keywords
Download to read the full chapter text
Chapter PDF
References
Bandos, T., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transaction on Geoscience and Remote Sensing 47(3), 862–873 (2009)
Berge, A., Solberg, A.: Structured gaussian components for hyperspectral image classification. IEEE Transaction on Geoscience and Remote Sensing 44(11), 3386–3396 (2006)
Scholkopf, B., Smola, A.: Learning With KernelsSupport Vector Machines, Regularization, Optimization and Beyond, Cambridge, MA. MIT Press Series (2002)
Cawley, G.C., Talbot, N.L.C.: Sparse multinomial logistic regression via bayesian L1 regularisation. In: Advances in Neural Information Processing Systems (2007)
Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43, 1351–1362 (2005)
Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.: Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing 46(11), 3804–3814 (2008)
Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C., Trianni, G.: Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment (2009)
Camps-Valls, G., Marsheva, T.B., Zhou, D.: Semi-supervised graph-based hyperspectral image classification. IEEE Transaction on Geoscience and Remote Sensing 45(10), 3044–3054 (2007)
Gao, Y., Chua, T.-S.: Hyperspectral image classification by using pixel spatial correlation. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part I. LNCS, vol. 7732, pp. 141–151. Springer, Heidelberg (2013)
Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3D object retrieval and recognition with hypergraph analysis. IEEE Transactions on Image Processing 21(9), 4290–4303 (2012)
Marconcini, M., Camps-Valls, G., Bruzzone, L.: A composite semisupervised svm for classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 6(2), 234–238 (2009)
Gu, Y., Wang, C., You, D., Zhang, Y., Wang, S., Zhang, Y.: Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing 50(7), 2852–2865 (2012)
Park, H.S., Ra, J.B.: Homogeneous region merging approach for image segmentation preserving segmentic object contours. In: Proceedings of the International Workshop on Very Low Bitrate Video Coding, Chicago, IL, pp. 149–152 (1998)
Fablet, R., Boucher, J.-M., Augustin, J.-M.: Region-based image segmentation using texture statistics and level-set methods. In: Acoustics, Speech and Signal Processing (2006)
Nock, R., Nielsen, F.: Statistical region merging. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)
Cevahir, C., Aydin Alatan, A.: Region-based image segmentation via graph cuts. In: 15th IEEE International Conference on Image Processing, pp. 2272–2275 (2008)
Li, J., Bioucas-Dias, J.M.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote sensing 48(11), 4085–4098 (2010)
Kulis, B., Dhillon, S.B.I., Mooney, R.: Semi-supervised graph clustering: a kernel approach. In: Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany (2005)
Li, J., Bioucas-dias, J.M., Plaza, A.: Hyperspectral image segmentation using a new bayesian approach with active learning. IEEE Transaction on Geoscience and Remote Sensing 49(10), 3947–3960 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, N., Lu, K., Wang, Y., Gao, Y. (2013). Semi-supervised Remote Sensing Image Segmentation Using Dynamic Region Merging. In: Kurosu, M. (eds) Human-Computer Interaction. Towards Intelligent and Implicit Interaction. HCI 2013. Lecture Notes in Computer Science, vol 8008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39342-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-39342-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39341-9
Online ISBN: 978-3-642-39342-6
eBook Packages: Computer ScienceComputer Science (R0)