In: Wagner W„ Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010,1 APRS, Vol. XXXVIII, Part 7B
562
Ghanbarian, M. A., 2007. Tectonic and Geoelectrical studies of
Salt domes in Southern fars with emphasis on Groundwater
quality; MSc thesis, Dept. Of Earth Sciences, Shiraz
University, Iran, pp. 230.
Green, A. A., Berman, M., Switzer, B., and Craig, M. D., 1988.
A transformation for ordering multi spectral data in terms of
image quality with implications for noise removal: IEEE
Transactions on Geoscience and Remote Sensing, v. 26, no. 1,
pp. 65 - 74.
Haykin, S., 1994. Neural networks: A comprehensive
foundation. Upper Saddle River, NJ7 Prentice Hall.
Heermann, P.D., and Khazenie, N., 1992. Classification of
multispectral remote sensing data using a backpropagation
neural network. IEEE Trans. Geosci. Remote Sens., 30, pp. 81-
88.
Hick, P.T., Russell, W.G.R., 1990. Some spectral
considerations for remote sensing of soil salinity. Aus. J. Soil
Res., 28, pp. 417-431.
Hunt, G., Salisbury J.W., 1976. Visible and near infrared
spectra of minerals and rocks: XII. Metamorphic rocks. Modern
Geology, 5, pp. 219-228.
Hen Hu, Y and Neng Hwang, J., 2002. Hand book of neural
network signal processing. CRC Press, pp. 1-1-1-30.
Hu, X., and Weng, Q., 2009. Estimating impervious surfaces
from medium spatial resolution imagery using the self
organizing map and multi-layer perceptron neural networks.
Remote Sensing of Environment, 113, pp. 2089-2102.
Ji, C. Y. (2000). Land-use classification of remotely sensed data
using Kohonen selforganizing feature map neural networks.
Photogrammetric Engineering & Remote Sensing, 66, pp.
1451-1460.
Kanellopoulos, I., and Wilkinson, G.G. 1997. Strategies and
best practice for neural network image classification. Int. J.
Remote Sens., 18, pp. 711-725.
Kavzoglu, T., and Mather, P. M. 2003. The uses of
backpropagating artificial neural networks in land cover
classification. International Journal of Remote Sensing, 24(23),
pp. 4907-4938.
Kent, P.E., 1979. The emergent Hormuz salt diapirs of southern
Iran, J. Petrol. Geol, 2, pp. 117-144.
Kruse, F.A., 1988. Use of airborne imaging spectrometer data
to map minerals associated with hydrotheramlly altered rocks in
the Northern Grapevine Mountains, Nevada, and California.
Remote Sens. Environ. 24, pp. 31-51.
Liu, J., Goering, C.E., Tian, L., 2001. A neural network for
setting target com yields. Trans. ASAE 44 (3), pp. 705-713.
Mettemicht, G.I., Zink, J.A., 2003. Remote sensing of soil
salinity: potential and constraints. Remote sensing of
Environment, 85, pp. 1-20.
Monger, M., 2002. Soil classification in arid lands with
Thematic Mapper data. Terra, 20(2), pp. 89-100.
Mougenot, B., Pouget, M., Epema, G.F., 1993. Remote sensing
of salt-affected soils. Remote Sensing reviews, 1, pp. 241-259.
Jing, Q. C. L and Panahi, A., 2006. Principal component
analysis with optimum order sample correlation coefficient for
image enhancement. International Journal of Remote
Sensing,21 {\в), pp. 3387 - 3401.
Richards, J.A., 1984. Thematic mapping from multitemporal
image data using principal components transformation. Remote
Sensing of Environment, 16, pp. 35-46.
Roosta, H., Farhudi, R., Afifi M., 2007. Comparison between
Sub-pixel Classifications of MODIS images: Linear Mixture
Model and Neural Network Model, WSEAS Transactions on
Environment and Development,
Rumelhart, D.E, and MacClelland, J.L., 1986. Parallel
Distributed Processing. Explorations in the Microstructureof
Cognition, vol. I, MIT Press, Cambridge, MA.
Sabins, F.F., 1997. Remote Sensing, Principles and
Interpretation, third ed, Freeman, New York, pp. 494.
Stocklin, J., 1974. Possible ancient continental margin in Iran,
In: Burk, C.A., Drake, C.L. (Eds.), The Geology of Continental
Margins. Springer, Berlin, pp. 873-887.
Tavakícoli, H., 2008. Comparison of TM and ASTER datasets
for enhancement of lithological units of salt domes, a case study
from Firouzabad area; MSc thesis, Dept. Of Earth Sciences,
Shiraz University, Iran, pp.152.
Verbeke, L.P.C., Vancoillie, F.M.B., De Wulf, R.R., 2004.
Reusing back-propagation artificial neural networks for land
cover classification in tropical savannahs. Int. J. Remote Sens,
25, pp. 2747-2771.
Yuan, H., Van Der Wiele, C. F., Khorram, S., 2009. An
Automated Artificial Neural Network System for Land
Use/Land Cover Classification from Landsat TM Imagery.
Remote Sens. 1, pp. 243-265.
Zadneek, T., 2008. Environmental effects of Konarsiah Salt
dome on pollution of water resource of Firouzabad area; MSc
thesis, Dept. Of Earth Sciences, Shiraz University, Iran,
pp.120.
Zhai, Y., Thomasson, J.A., Boggess III, J.E., Sui, R., 2006. Soil
texture classification with artificial neural networks operating
on remote sensing data. Computers and Electronics in
Agriculture 54 (2), pp. 53-68.
Ziaii, M., a,. Pouyan, A.A., Ziaei, M., 2009. Neuro-fuzzy
modelling in mining geochemistry: Identification of
geochemical anomalies. Journal of Geochemical Exploration.
100, pp. 25-36.
Zinck, J. A., 2001. Monitoring salinity from remote sensing
data. In R. Goossens, & В. M. De Vliegher (Eds.), Proceedings
of the 1 st Workshop of the EARSeL Special Interest Group on
Remote Sensing for Developing Countries (Belgium: Ghent
University), pp. 359-368.