AN ANALYZE OF A BACKPROPAGATION NEURAL NETWORK IN THE IDENTIFICATION OF CRITICAL LAND BASED ON ALOS IMAGERY Nursida Arif 1 and Projo Danoedoro 2 1 Muhammadiyah University Of Gorontalo (UMG) Jl.Mansoer Pateda,Pentadio Timur,Gorontalo, nursida.arif@um-gorontalo.ac.id 2 Faculty Of Geography,Gadjah Mada University (UGM) Jl.Kaliurang,Skiep Utara,Bulaksumur,Yogyakarta projo.danoedoro@geo.ugm.ac.id ABSTRACT Studies on the use of artificial neural network method in spatial data processing are still very rare compared to other classification methods.artificial Neural Network (ANN) system that can combine spectral and non spectral data is expected to improve the accuracy of results. In testing the network, changing network parameters including every hidden layer and the number of iterations gives an effect associated with the resulting level of accuracy in the identification of critical lands.this paper focuses on the backpropagation algorithm of ANN in the identification of crtical land. Merging spectral and non spectral data in this study increased the overall accuracy when viewed from all simulations conducted to compare the results of classification using spectral data. In testing the network, changing network parameters including every hidden layer and the number of iterations gives an effect associated with the resulting level of accuracy in the identification of critical lands. Keywords: artificial neural network, backpropagation, classsification, hiden layer, critical land INTRODUCTION Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data obtained with a device without direct contact with the object, area or phenomenon studied (Lillesand, 2004). In this case the recorded is the earth's surface for a variety of human interests. The energy inte`raction from the object to the sensor through the atmosphere and many atmospheric interactions occur, among others: scattering and absorption. By utilizing remote sensing and GIS data, information about critical land can be known early hence such data can be used as evaluation material for planning in the handling of critical lands. ALOS imagery can provide solutions in the study of natural resources. This is due to its medium spatial resolution of 10 m, as well as having good data continuity. Data Obtained from ALOS imagery is then be analyzed to identify critical lands. Identification of critical land based on satellite imagery is strongly influenced by non-spectral data such as slope. Artificial Neural Networks is one of machine learning algorithms which have characteristics resembling human nerve tissue (Puspitaningrum, 2006) Artificial Neural Network (ANN) system that can combine spectral and non spectral data is expected to improve the accuracy of results. In testing the network, changing network parameters including every hidden layer and the number of iterations gives an effect associated with the resulting level of accuracy in the identification of critical lands. ANN is one of the artificial intelligence models that are designed similar to biological neurons in humans who have the ability to tolerate mistakes and do whatever training patterns are included, learn and recognize something, even if there are irregularities. ANN has high potential to solve difficult problems simulated using logic, expert systems analysis techniques, and software technology. For example, ANN can analyze large amounts of data to determine the pattern and characteristics in situations that are not recognized by existing rules. In this paper had been use backpropagation algorithm, this algorithtm most useable because exercise process based on interconections simple basic where if give wrong output, then balance (weight) will change to correct so value of galat can be ower to close the right value.
MATERIALS AND METHODS / EXPERIMENTAL The process of geometric correction used a projection of Universal Transverse Mercator (UTM) WGS-84 datum, Zone 49S which is a zone of he area of research by using a 3 rd order polynomial transformation of 24 ground Control Point with reference to the Indonesia topographic maps scale 1:25.000 of the study area. Geographically the study area is a hilly area with dry farming areas and is prone to landslides. In this study, the study area was the districts of Imogiri and Dlingo. Dlingo district is located in the area to east of the downtown Bantul,Yogyakarta. Training area Selection of the sample (training area) is to determine the sample to be used in the ANN based on the parameters of critical lands.this training area was then classified using IDRISI software by ANN method. The number of pixels that was trained in the network is 1070 pixels by seven (7) band combinations of in the input layer. The combination of both using 7 bands consisting of blue channel, green channel, red channel, the near infrared channel, NDVI, slope, and depth of solum. Non-spectral data was first converted into a value of 0-255 (8 bits) and then converted to a value of 0-1 so that non-spectral data can be combined with spectral data. Merger was intended to obtain better classification results. Backpropagation-Artificial neural network Backpropagation algorithm using the error output to modify the weights in the backward direction or so-called backward (Fausset, 1994). architecture of backpropagation. Figure 1.Backpropagation Architecture The non-parametric supervised classification algorithm of Artificial Neural Networks was used. Network parameters to be determined are: 1. Iteration is the number of repetitions performed during the data training in the classification of critical lands. This study will be try to use the number of iterations as 5000 times to 19 000 times. Based on earlier research in Samudra (2007) used iteration of 4000 times with an accuracy of (76.85%) using spectral and non spectral data. With the 5000 iterations, it s expected to improve the accuracy. The number of iterations greatly affects the time spent in training and network testing. 2. Learning rate, in this system, the learning constant that was used is 0.001 to 0.05. Learning Constant can be changed in order to obtain the most optimum constant value for the training process. Basically before the training is done, it is difficult to determine the magnitude of the most optimal training pace. The higher the average learning value, the faster the learning process, but this will increase the miscalculation value between the learning outcomes to inputs, such errors can be reduced by adjusting the momentum of learning (training momentum) with a value of less than 1.
3. Activation method used was the sigmoid activation function, since this function can compress the unlimited signal into the limited signal in the threshold region ranged from 0 to 1; in addition to that, the resulting output value is not negative value. 4. Momentum value, in the interval value of zero to one. Entering the momentum value greater than zero will give the opportunity for setting the pace of training (training rate) which is higher without the occurrence of oscillations. 5. Value, iteration will stop if the RMS value < tolerance limit value set on the network or the number of epoch that has reached a specified threshold. In this study the RMS was set at 0.0001 until 0.002 in hopes of getting better classification results. 6. Hidden layer is the layer that receives the response in the form of weights from input layer to be forwarded to the output layer. The number of hidden layers is determined through experimentation; at the beginning of the training hidden layer one and two will be tested. Accuracy Assessment The sampling method used in this study was stratified random sampling. Field checking was carried out to obtain the land conditions to be used in the accuracy assessment of the method used. Accuracy assessment of modeling results was done by comparing the results of the identification of critical land using ANN with field observations. Observations were made using the four parameters: slope, vegetation cover conditions using NDVI, soil depth and soil texture. Accuracy assessment will be applied by using the overall accuracy, producer s accuracy, user's accuracy (Jensen,1996) Analysis of Results The results of the identification of critical land using ANN methods require evaluation and discussion to determine network performance. Analysis conducted in this study includes: (1) analyze the ability of the process and results of identification using back propagation algorithm by combining spectral and non spectral data, (2) analyze the influence of network parameters such as iteration, the number of hidden layer used in accelerating or slowing down network performance. In some previous studies, network parameters provide a considerable influence on the performance of the network. Analysis of the results is the final stage of the comparison of ANN with field data, (3) analyze the ability of ANN method when compared with conventional methods previously used. RESULTS AND DISCUSSION ANN parameters work following workflow backpropagation algorithm. Wherein when the error is larger than a given tolerance value in the training phase of this error will be propagated back (backward) from the output layer to each of the cells in the previous layer. Several previous studies using the backpropagation algorithm is Sudheer et al (2010), Mas, JF (2003), Sari (2010). Sudheer et al (2010) compared the methods of Multilayer Perceptron (MLP) using the backpropagation algorithm with an accuracy of 66% to 80%. From observations on the experiments carried out with 20 simulations can be concluded that each ANN input parameters affect the accuracy of results. The right combination will yield high accuracy with a stable network. Some parameters have a relationship, so the addition or reduction in value of one parameter often affects the value of the other parameters. As the number of iterations and the RMS error, the smaller the iteration or the less repetition is done then the higher RMS error on the training and testing. Conversely, the more the number of iterations is done, then the smaller the RMS error. This relationship is shown in the following figure.
Image Classification Figure 2. RMS Error vs.iterations using seven (7) bands Tests were conducted to test the critical land classification results using Neural Networks with field data. Data to be tested is a simulation that has the best architecture in the training data that is ANN 2 on 4 channels, ANN 18 on 5 channels and ANN 21 on 7 channels. The number of samples tested i.e. 24 samples was not involved in the training data. Comparison of accuracy assessment can be seen in the following table. J.F. Mas, 2003 mentions that ANN architecture is considered to possess the best performance if the classification accuracy of above 70% is achieved. Highest accuracy using 7 channels (band1,band 2, band3, band 4,NDVI, slope, and depth of solum) occurred at ANN with an accuracy of 76.64%. Crtical Rather Crtical Potentially Critical Not Crtical Figure 3.Classification results of Backpropagation Tabel 1.Error matrix and kappa coefficient of backpropagation Ground Truth User Total Rather Potentially Accuracy (pixel) Classes Critical Critical critical Not critical (%) Critical 147 20 26 0 76 193 Rather Critical 36 203 36 15 70.0 290 Potentially critical 67 22 370 0 80.6 459 Not Critical 0 22 0 106 82.8 128 Total 250 267 432 121 1070 Producer Accuracy 58.8 76.0 85.6 87.6 Overll Accuracy 77.20
Tabel 2.Assessment of the ANN Matrix on ANN 21 using 7 bands Ground Truth ANN Method Total Critical Rather Potentially Not critical critical critical Critical 1 0 0 0 1 Rather critical 0 9 1 0 10 Potential critical 1 2 9 0 2 Not critical 0 0 0 1 1 total 2 11 10 1 24 Accuracy (%) 83.33 CONCLUSION Based on the analysis and discussion of the previous explanation, it can be made of the following conclusions: 1. Merging spectral and non spectral data in this study increased the overall accuracy when viewed from all simulations conducted i.e. 20 simulations. Highest accuracy was in the simulation that used 7 (seven) channels (merging spectral data and non-spectral) with an accuracy of 83.33%. 2. ANN parameters affect the resulting accuracy. For 7 (seven) channels the best architecture occurred in ANN simulation with learning rate of 0.01, hidden layer 1, the momentum of 0.04, 0.001 and RMS 19000 iterations. Studies on the use of artificial neural network method in spatial data processing are still very rare compared to other classification methods, so it still needs to be done again more diverse experiments to develop this method. Process and the results obtained from this study can be used as reference for the development of further studies on Artificial Neural Networks. ACKNOWLEDGMENT Authors would like to thank head and all of staff of PUSPICS at Gadjahmada University for providing the ALOS data, and to thank Andrew Mulabbi for making corrections in english version of this paper. Finally the author would like to thank Nur Mohammad Farda,M.Cs for improving the article. REFERENCES [1]. Fausett, L., 1994,Fundamentals Of Neural Networks Architectures, Algorithms, and Applications, Prentice-Hall, New Jersey. [2]. Jensen,J.R.,1996,Introductionary Digital Image Processing : A Remote SensingPerspective, London:Prentice Hall [3]. Kusumadewi, S.,2004,Membangun Jaringan Syaraf Tiruan Menggunakan MATLAB&EXCEL LINK, Penerbit Graha Ilmu, Yogyakarta [4]. Lillesand, T.M., dan R,W. Kiefer, 2004,Penginderaan Jauh dan Interpretasi Citra, Gadjah Mada University,Yogyakarta [5]. Mas,J.F.,2003, Mapping Landuse/Cover in Tropical Coastal Area Using Satellite Sensor Data, GIS and Artificial Neural Network. Estuarine, Coastal and Shelf Science, (59), 219-230 [6]. Puspitaningrum,D.,2006, Pengantar Jaringan Syaraf Tiruan, Andi Offset, Yogyakarta [7]. Sudheer,K.P.,Gowda P.,Chaubey I., and Howell T.,2010,Artificial Neural Network Approach for mapping Contrasting Tillage Practices. Availiable online: www.mdpi.com/journal/remotesensing.pdf (accessed on 1st January 2011) [8]. Sari,D.A.,2010,Uji Akurasi Klasifikasi Jaringan Syaraf Tiruan Pada Pemetaan Tutupan Lahan Sebagian Wilayah Yogyakarta, Skripsi Fakultas Geografi Universitas Gadjah Mada, Yogyakarta [9]. Samudra.,2007,Kajian Kemampuan Metode Jaringan Syaraf Tiruan untuk Klasifikasi Penutup Lahan dengan Menggunakan Citra ASTER,Fakultas Geografi Universitas Gadjah Mada,Yogyakarta