This paper focuses on
prediction of change in agricultural lands by using ART2 algorithm. The
existing method used ENVI and ARCGIS software to predict the changes in land,
which showed less accuracy due to human errors. To overcome this user friendly
GUI based ART2 algorithm has been developed in java to obtain more accuracy in
prediction of changes in land. The input is satellite temporal images of the
years 1990 and 2014. By using the ART2 algorithm, the input images of the years
1990 and 2014 are classified, where the features are identified to form
cluster. The clustered image is given as input and pixel to pixel comparison
method in ART2 is implemented in java, for detecting the changes in
agricultural lands. The comparison results of ENVI and ARCGIS and GUI based
ART2 with？in situ？data show that the prediction of
changes in agricultural land is more accurate in the case of GUI based ART2
implementation.
References
[1]
Hussain, M. (2013) Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106. http://dx.doi.org/10.1016/j.isprsjprs.2013.03.006
[2]
Fkirin, M.A. (2009) Change Detection Using Neural Network with Improvement Factor in Satellite Images. American Journal of Environmental Sciences, 5, 706-713.
[3]
Haykin, S. (1994) Neural Networks, Comprehensive Foundation. College Publishing Company, New York.
[4]
da Silva, N.C. (2012) Change Detection Software Using Self-Organizing Feature Maps. Brazilian Journal of Geophysics, 30, 505-518.
[5]
Heerman, P.D. (1991) Classification of Multi-Spectral Remote Sensing Data Using Back-Propagation Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 30, 81-88. http://dx.doi.org/10.1109/36.124218
[6]
Ahmadizadeh, S. (2014) Land Use Change Detection Using Remote Sensing and Artificial Neural Network: Application to Birjand, Iran. Computational Ecology and Software, 4, 276-288.
[7]
Deilmai, B.R. (2014) Comparison of Pixel-Based and Artificial Neural Networks Classification Methods for Detecting Forest Cover Changes in Malaysia, Earth and Environmental Science. 8th International Symposium of the Digital Earth (ISDE8).
[8]
Pijanowski (2002) Using Neural Networks and GIS to Forecast Land Use Changes: A Land Transformation Model. Computers, Environment and Urban Systems, 26, 553-575.
[9]
Abdullah, F. (2013) Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review. Advances in Remote Sensing, 193-204.
[10]
Dam, H. (2008) Neural-Based Learning Classifier Systems. IEEE Transactions on Knowledge and Data Engineering, 20, 26-39. http://dx.doi.org/10.1109/TKDE.2007.190671
[11]
Long Dal, X. (1999) Remotely Sensed Change Detection Based on Artificial Neural Networks. Photogrammetric Engineering & Remote Sensing, 65, 1187-1194.
[12]
Mas, J.F. (2008) The Application of Artificial Neural Networks to the Analysis of Remotely Sensed Data. International Journal of Remote Sensing, 29, 617-663. http://dx.doi.org/10.1080/01431160701352154
[13]
Pajares, G. (2012) A Hopfield Neural Network for Image Change Detection. IEEE Transactions on Neural Networks, 17, 1250-1264.
