Geography Reference
In-Depth Information
Mannan, B., Rot, J., & Ray, A. K. (1998). Fuzzy ARTMAP supervised classification of
multispectral
remotely-sensed
images.
International
Journal
of
Remote
Sensing,
19(4),
767-774.
Marcal, A. R. S., Borges, J. S., Gomes, J. A., & Da Costa, J. F. P. (2005). Land cover update by
supervised classification of segmented ASTER images. International Journal of Remote
Sensing, 26(7), 1347-1362.
Markham, B. L., & Barker, J. L. (1987). Thematic mapper bandpass solar exoatmospheric
irradiances. International Journal of Remote Sensing, 8(3), 517-523.
Mather, P. M. (2004). Computer processing of remotely-sensed images: An introduction (3rd ed.).
Chichester: Wiley.
Mausel, P. W., Kramber, W. J., & Lee, J. K. (1990). Optimum band selection for supervised
classification of multispectral data. Photogrammetric Engineering and Remote Sensing, 56,
55-60.
McCoy, R. M. (2005). Field methods in remote sensing. New York, NY: Guilford Press.
Medhavy, T. T., Sharma, T., Dubey, R. P., Hooda, R. S., Mothikumar, K. E., Yadav, M., et al.
(1993). Crop classification accuracy as influenced by training strategy, data transformation
and spatial resolution of data. Journal of the Indian Society of Remote Sensing, 21(1), 21-28.
Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with
support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8),
1778-1790.
McCloy, K. R. (1995). Resource management system: Process and practice. London: Taylor and
Francis.
Moran, M. S., Jackson, R. D., Slater, P. N., & Teillet, P. M. (1992). Evaluation of simplified
procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote
Sensing of Environment, 41(2-3), 169-184.
Nielsen, A. A. (2007). The regularized iteratively reweighted MAD method for change detection
in multi- and hyperspectral data. IEEE Transactions on Image Processing, 16(2), 463-478.
Nilsson, N. J. (1965). Learning machines. New York: McGraw-Hill.
Olsson, K. (1985). Remote sensing for fuelwood resources and land degradation studies in
Kordofan, the Sudan. Avhandlingar C: Meddelande från Lunds Universitets Geografiska
Institution.
Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing.
International Journal of Remote Sensing, 26(5), 1007-1011.
Pal, M., & Mather, P. M. (2006). Some issues in the classification of DAIS hyperspectral data.
International Journal of Remote Sensing, 27(14), 2895-2916.
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition,
26(9), 1277-1294.
Paola, J. D., & Schowengerdt, R. A. (1995). A review and analysis of back propagation neural
networks for classification of remotely sensed multispectral imagery. International Journal of
Remote Sensing, 16(16), 3033-3058.
PCI. (2001). Xpace help system (softcopy, version 9.1). Ontario: Richmond Hill.
Price,
J.
C.
(2003).
Comparing
MODIS
and
ETM+ data
for
regional
and
global
land
classification. Remote Sensing of Environment, 86(4), 491-499.
Qiu, F., & Jensen, J. R. (2004). Opening the black box of neural networks for remote sensing
image classification. International Journal of Remote Sensing, 25(9), 1749-1768.
Richards, J. A., & Jia, X. (2003). Remote sensing digital image analysis: An introduction (3rd
ed.). New York: Springer.
Richter, R. (1996a). A spatially adaptive fast atmospheric correction algorithm. International
Journal of Remote Sensing, 17(6), 1202-1214.
Richter, R. (1996b). Atmospheric correction of satellite data with haze removal including a haze/
clear transition region. Computers & Geosciences, 22(6), 675-681.
Richter, R. (2011). Atmospheric/topographic correction for satellite imagery: ATCOR-2/3 User
Guide. DLR IB 565-01/11, Germany: Wessling.
Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.
Search WWH ::




Custom Search