TY  - JOUR
T1  - Classification of Al-Hammar Marshes Satellite Images in Iraq using Artifical Neural Network
Based on Coding Representation
AU - S. Mahdi, Mohammed AU - Abdulla, Ashraf S. AU - Q. Al-Abudi, Bushra 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 11
SP  - 241
EP  - 249
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.241.249
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.241.249
KW  - Classification
KW  -Landsat satellite images
KW  -back propagation artificial neural network
KW  -Al-Hammar marshes
KW  -MATLAB
AB  - In this study, Landsat satellite images of
hammar marshes and surrounding district in (Dhi Qar)
province in the South of Iraq are classified by Back
Propagation Artificial Neural Network (BPANN) for
years 1991, 2000, 2015 and 2017. Firstly, Principle
Components Analysis (PCA) is applied on six bands of
these satellite images using MATLAB programming and
the information of all six bands concentrated in first three
principle component and then blended to form integrated
image. Then the integrated image is classified using
proposed method (BPANN) method based on encoding
elements. In this proposed method (BPANN) there are
two paths are considered training and classification. The
estimated coded descriptors are input to the training and
classification phases of the ANN. It is intended to prove
that the encoding capabilities can lead to improve the
classification accuracy. The training is useful to indicate
the basic information about image classes that represented
by some specified statistical features while the
classification uses the same features to produce the final
classification results in terms of training results. Results
evaluation is carried out for validation purpose. Then,
quantitative and qualitative analysis is estimated to
evaluate the performance of the proposed classification
method.. The artificial neural network showed valued
ability for classifying satellite images.
ER  -