The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
352
enhance weak heavy metal stress information, and Gaussian
membership function is adopted because of its non-constant
differentiable character. Tow standards, output error and the
width of Gaussian membership function, are used to decide
whether a new fuzzy rule should be added into this system. The
significance of each rule is evaluated to decide whether a rule
should be deleted. A training data set which is composed of
250 samples obtained from MODIS data is applied to adjust
network structure and to generate fuzzy rules. On the basis of
seven fuzzy reasoning rules, this system can achieve 95%
accuracy. According to the result of experiment, the advantages
of this DFNN model are summarized as follows:
1. It offers faster convergence and is less sensitive to
both training and testing datasets;
2. It substantially decreases the number of hidden
neurons which is crucial in the optimization of network
structure, as fuzzy rules are generated or deleted
according to the network performance and the significance
of each rule;
3. It is capable of extracting crop heavy metal stress
information with reasonable accuracy, and thus it could be
used as an effective tool in monitoring and managing
agricultural environment.
ACKNOWLEDGMENT
This work is under the auspice of National High-tech R&D
Program of China (863 program) (2007AA12Z174) and
National Natural Science Foundation of China (40771155).
REFERENCES
Atkinson P.M., Tatnall, A.R.L, 1997. Neural networks in
remote sensing. Int. J. Remote Sens, 18, pp. 699-709.
D.P. Kanungo, M.K. Arora, S. Sarkar, R.P. Gupta, 2006.A
comparative study of conventional, ANN black box, fuzzy and
combined neural and fuzzy weighting procedures for landslide
susceptibility zonation in Darjeeling Himalayas. Engineering
Geology, 85, pp. 347 -366.
Gong Peng, 1996. Integrated analysis of spatial data for
multiple sources: using evidential reasoning and artificial
neural network techniques for geological mapping.
Photogramm. Eng. Remote Sensing, 62, pp. 513-523.
Hasi Bagan, et al., 2003. Study of ASTER data classification
using self-organizing neural network method. Advance in
Earth Sciences, Vol. 18, No.3, pp. 345-350.
H. Noh, Q. Zhang, B. Shin, S. Han, L. Feng , 2006. A neural
network model of maize crop nitrogen stress assessment for a
multi-spectral imaging sensor. Biosystems Engineering, 94(4),
pp. 477-485.
Jesus Favela, Jorge Torres., 1998. A two-step approach to
satellite image classification using fuzzy neural networks and
the ID3 learning algorithm. Expert System with Application,
14, pp. 211-218.
Jiao Yunqing, Wang Shixin, Zhou Yi, Fu Qinghua, 2007.
Super-resolution target identification from remotely sensed
imagery using Hopfield neural network. Journal of System
Simulation, Vol.19, No.14, pp. 3223-3225.
Kwokwing Chau, 2006. A review on the integration of
artificial intelligence into coastal modeling. Journal of
Environmental Management, 80, pp. 47-57.
Lehmann, F., Rothfuss, H., Werner K., 1991. Imaging
spectroscopy data used for geological and environmental
analysis in Europe. In: Summaries of the Third Annual JPL
Airborne Geoscience Workshop, 20 and 21 May 1991. JPL,
Pasadena, pp. 62-71.
L. Kooistr, E.A.L. Salas, 2004. Exploring field vegetation
reflectance as an indicator of soil contamination in river
floodplains. Environment Pollution, 127, pp. 281-290.
Martin Hellmann, Gunther Jäger, 2002. Fuzzy rule based
classification of polarimetric SAR data. Aerospace Science and
Technology, 6, pp. 217-232.
Min Han, Yannan Sun, Yingnan Fan, 2008. An improved
fuzzy neural network based on T-S model. Expert Systems
with Applications, 34, pp. 2905-2920.
Mohammed, G.H., Noland, T.L., Irving, D., Sampson, P.H.,
Zarco Tejada, P.J., Miller, J.R., 2000. Natural and Stress-
Induced Effects on Leaf Spectral Reflectance in Ontario
Species. Forest Research Report No. 156, Ontario Ministry of
Natural Resources, Canada.
Ralf Wieland, Wilfried Mirschel, 2008. Adaptive fuzzy
modeling versus artificial neural networks. Environmental
Modelling & Software, 23, pp. 215-224.
Shao Dong, Zhou Zhihua, 1999. Research of fuzzy neural
network. Application Research of Computers, Vol.7, pp.
1,2,21.
Sommer, S., Hill, J., Megier, J., 1998. The potential of remote
sensing for monitoring rural land use changes and their effects
on soil conditions. Agriculture Ecosystems and Environment,
67, pp. 197-209.
Sucharita Gopal, Curtis E. Woodcock, Alan H. Strahler, 1999.
Fuzzy Neural Network Classification of Global Land Cover
from a 1° AVHRR Data Set. REMOTE SENS. ENVIRON., 61,
pp.230-243.
Wu Yifan, 2004. A data mining algorithm based on fuzzy
neural network’s pattern classification. Journal of Changsha
Communications University, Vol 20, No.l, pp. 45-49.
Yunzhao Wu, Jun Chen, Xinmin Wu, Qingjiu Tian,Junfeng Ji,
Zhihao Qin, 2005. Possibilities of reflectance spectroscopy for
the assessment of contaminant elements in suburban soils.
Applied Geochemistry, 20, pp. 1051-1059.
Zhang Qiang, Zhou Qiusheng, 2006. Remote sensing image
classification and study using a kind of fuzzy neural networks.
Engineering of Surveying and Mapping, Vol 15, No.5, pp. 42-
46.