Introduction
Remote sensing of the earth is becoming a commonplace technology which is growing driven by the combined advances in sensor technologies and image and data processing.
In the near future the international proliferation of remote sensing devices will require an ever increasing number of high–resolution systems with lightweight instruments for application in small satellites and light planes, as well as a reduction of costs. One important tool used in remote sensing is imaging spectroscopy, also known as multi, hyper or ultraspectral remote sensing. It consists in the acquisition of images where for each spatial resolution element a part of the electromagnetic spectrum sampled at different rates is measured [1,2].
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References
Wiemker, R., Speck, A., Kulbach, D., Spitzer, H., Bienlein, J.: Unsupervised Robust Change Detection on Multispectral Imagery Using Spectral and Spatial Features. In: Proc. of 3rd International Airborne Remote Sensing Conference and Exhibition, vol. I, pp. 640–647 (1997)
Sabins, F.F.: Remote Sensing: Principles and Interpretation. John Wiley and Sons, Toronto (1994)
Campbell, J.: Introduction to Remote Sensing. The Guilford Press, New York (1996)
Sanders, L.C., Raqueno, R.V., Schott, J.R.: Atmospheric correction algorithm for hyperspectral imagery. In: Proceedings of SPIE, Image and Signal Processing for Remote Sensing, vol. 3871, pp. 2–9 (1999)
Guatieri, J.A., Bechdol, M., Chettri, S., Robinson, J.W., Garegnani, J., Vermeulen, A., Antonille, S.: From Spectra to Cassification. In: 1st International Symposium on Hyperspectral Analysis, Caceres (Spain) (2000)
Yamazaki, A., Imanaka, M., Shikada, M., Okumura, T., Kawata, Y.: An atmospheric correction algorithm for space remote sensing data and its validation. In: Proc. of IGARSS 1997, vol. 4, pp. 1899–1901 (1997), ISBN: 0-7803-3836-7
Thome, K., Palluconi, F., Takashima, T., Masuda, K.: Atmospheric Correction of ASTER. IEEE Transactions on geoscience and remote sensing 36(4), 1199–1211 (1998)
Liang, S., Fang, H.: An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery. IEEE Geoscience and remote sensing letters 1(2), 112–117 (2004)
Rencz, A. (ed.): Manual of Remote Sensing. John Wiley and Sons Inc., New York (1999)
Van der Meer, F.D., de Jong, S.M.: Spectral Mapping Methods: many problems, some solutions. In: Habermeyer, M., Mueller, A., Holzwarth, S. (eds.) Proceedings of the 3rd EARSeL Workshop on Imaging Spectrometry, pp. 146–162 (2003), ISBN 2-90885-26-3
Hsu, P.H., Tseng, Y.H.: Feature Extraction for Hyperspectral Image. In: Proceedings of the 20th Asian Conference on Remote Sensing, vol. 1, pp. 405–410 (1999)
Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing. Academic Press, London (1994)
Ross, B.J., Gualtieri, A.G., Fueten, F., Budkewitsch, P.: Hyperspectral Image Analysis Using Genetic Programming Source. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1196–1203 (2002), ISBN:1-55860-878-8
Schouten, T.E., Klein, M.S.: Fuzzy classification of spectra. In: Neural networks: Best practice in Europe, pp. 198–201. World Scientific, Singapore (1997)
Plaza, J., Martínez, P., Pérez, R., Plaza, A.: Nonlinear neural network mixture models for fractional abundante estimation in AVIRIS hyperspectral images. In: Proc. of AVIRIS Airborne Earth Science Workshop, California (2004)
Nascimento, J.M.P., Bioucas Dias, J.M.: Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing 43(4), 898–910 (2005)
Bajorski, P.: Simplex projection methods for selection of endmembers in hyperspectral imagery. In: IGARSS 2004 Proceedings (2004), ISBN: 0-7803-8742-2
Bayliss, J., Gualtieri, J., Cromp, R.: Analyzing hyperspectral data with independent component analysis. In: Michael Selander, J. (ed.) Proc. SPIE AIPR Workshop, vol. 9 (1997)
Aguilar, P.L., Plaza, A., Martinez, P., Perez, R.M.: Endmember Extraction by a Self-Organizing Neural Network on Hyperspectral Images. In: VI International Conference on Automation, Robotics, Control and Vision (ICARCV), Singapore (2000)
Greiwe, A., Ehlers, M.: Combined Analysis of Hyperspectral and High resolution Image Data in an Object oriented Classification Approach. In: Proc. 5th International Symposium Remote Sensing of Urban Areas, Tempe, AZ, USA (2005)
Plaza, A., Martinez, P., Perez, R., Plaza, J.: Spatial spectral endmember extraction by multidimensional morphological operations. IEEE Transactions on Geoscience and Remote Sensing 40(9), 2025–2041 (2002)
Lee, C., Landgrebe, D.: Feature Extraction and Classification Algorithms for High Dimensional Data. Internal Report TR-EE 93-1. Purdue University (1993)
Merényi, E., Minor, T.B., Taranik, J.V., Farrand, W.H.: Quantitative Comparison of Neural Network and Conventional Classifiers for Hyperspectral Imagery. In: Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, Pasadena, CA, vol. 1 (1996)
Ghosh, J.: Adaptive and neural methods for image segmentation. In: Bovik, A. (ed.) Handbook of Image and Video Processing, ch. 4.10, pp. 401–414. Academic Press, London (2000)
Tadjudin, S., Landgrebe, D.: Classification of High Dimensional Data with Limited Training Samples. Ph.D. Thesis and School of Electrical & Computer Engineering Technical Report TR-ECE 98-8, Purdue University
Merényi, E.: The Challenges in Spectral Image Analysis: an Introduction, and Review of ANN Approaches. In: Proceedings of the 7th European Symposium on Artificial Neural Networks, ESANN (1999)
Duro, R.J., Crespo, J.L., Santos, J.: Training Higher Order Gaussian Synapses. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1606, pp. 537–545. Springer, Heidelberg (1999)
Prieto, A., Bellas, F., Duro, R.J., Lopez-Peña, F.: A Comparison of Gaussian Based ANNs for the Classification of Multidimensional Hyperspectral Signals. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 829–836. Springer, Heidelberg (2005)
Graña, M., Raducanu, B., Sussner, P., Ritter, G.: On Endmember Detection in Hyperspectral Images with Morphological Associative Memories. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527. Springer, Heidelberg (2002)
Landgrebe, D.: Indian Pines AVIRIS Hyperspectral Reflectance Data: 92av3c, http://makalu.jpl.nasa.gov/
Tadjudin, S., Landgrebe, D.: Covariance Estimation for Limited Training Samples. In: Proc. of Int. Geoscience and Remote Sensing Symp., Seattle (1998)
Tadjudin, S., Landgrebe, D.: Robust parameter estimation for mixture model. IEEE Trans. Geos. Rem. Sensing 38(1) (2000)
Boardman, J.W., Kruse, F.A., Green, R.O.: Mapping target signatures via partial unmixing of AVIRIS data. In: Fifth JPL Airborne Earth Science Workshop, vol. 1, pp. 23–26. JPL Publication 95-1 (1995)
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Duro, R.J., Lopez-Pena, F., Crespo, J.L. (2008). Using Gaussian Synapse ANNs for Hyperspectral Image Segmentation and Endmember Extraction. In: Graña, M., Duro, R.J. (eds) Computational Intelligence for Remote Sensing. Studies in Computational Intelligence, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79353-3_14
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