Abstract
This work is focusing on the hyperspectral imaging classification, which is nowadays a focus of intense research. The hyperspectral imaging is widely used in agriculture, mineralogy, or food processing to enumerate only a few important domains. The main problem of such image classification is access to the ground truth, because it needs the experienced experts. This work proposed a novel three-stage image segmentation method, which prepares the data for the classification and employs the active learning paradigm which reduces the expert works on image. The proposed approach was evaluated on the basis of the computer experiments carried out on the benchmark hyperspectral datasets.
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References
Bennett, K.P., Demiriz, A.: Semi-supervised support vector machines. In: Advances in Neural Information Processing Systems, pp. 368–374. MIT Press (1998)
Rajan, S., Ghosh, J., Crawford, M.M.: An active learning approach to hyperspectral data classification. IEEE T. Geoscience and Remote Sensing 46(4), 1231–1242 (2008)
Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE T. Geoscience and Remote Sensing 51(2), 844–856 (2013)
Krawczyk, B., Filipczuk, P.: Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition. Engineering Applications of Artificial Intelligence 31, 126–135 (2014)
Jackowski, K.: Multiple classifier system with radial basis weight function. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010, Part I. LNCS, vol. 6076, pp. 540–547. Springer, Heidelberg (2010)
Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities. Elsevier, December 2004
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)
Bengio, Y., Grandvalet, Y.: No unbiased estimator of the variance of k-fold cross-validation. J. Mach. Learn. Res. 5, 1089–1105 (2004)
Chi, M., Qian, Q., Benediktsson, J.A.: Cluster-based ensemble classification for hyperspectral remote sensing images. In: IGARSS, vol. 1, pp. 209–212. IEEE (2008)
Chi, M., Kun, Q., Benediktsson, J.A., Feng, R.: Ensemble classification algorithm for hyperspectral remote sensing data. IEEE Geoscience and Remote Sensing Letters 6(4) (2009)
Ceamanos, X., Waske, B., Benediktsson, J.A., Chanussot, J., Fauvel, M., Sveinsson, J.R.: A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data. Int. J. Image Graphics 1(4), 293–307 (2010)
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Ksieniewicz, P., Graña, M., Woźniak, M. (2015). Blurred Labeling Segmentation Algorithm for Hyperspectral Images. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_56
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DOI: https://doi.org/10.1007/978-3-319-24306-1_56
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