LAND COVER CLASSIFICATION OF ALOS DATA USING BACK-PROPAGATION NEURAL NETWORK MODELS ARIANI ANDAYANI

Transcription

1 LAND COVER CLASSIFICATION OF ALOS DATA USING BACK-PROPAGATION NEURAL NETWORK MODELS ARIANI ANDAYANI GRADUATE SCHOOL BOGOR AGRICULTURAL UNIVERSITY BOGOR 2008

2 STATEMENT Hereby I, Ariani Andayani, do declare that this thesis entitled Land Cover Classification of ALOS Data Using Back-propagation Neural Network Models is my own work and has not been submitted in any form for another degree or diploma programs (course) to any university or other institution. The content of the thesis has been examined by the advising committee and the external examiner. Bogor, November 2008 Ariani Andayani G

3 ABSTRACT ARIANI ANDAYANI (2008). Land Cover Classification of ALOS Data Using Back-propagation Neural Network Models. Under the supervision of I WAYAN ASTIKA and HARTANTO SANJAYA. Land cover information is vital for supporting decision concerning the management of the environment and for understanding the causes and trends of human and natural processes on the earth surface. The extraction of useful information (i.e. land cover) from satellite images, by means of classification, is one of the most important technical problems of remote sensing. The aim of this research is to develop land cover classification method of ALOS data using Back-Propagation Neural Network classifier a case study in Bogor Botanical Garden West Java. The targets of this research are: to investigate the effect of different input parameter to land cover classification result using Back-Propagation Neural Network and to assess accuracy of the classification result generated with different input and to compare accuracies to decide whether one model is superior to another. Bands rationing was used as input together with the original bands. This study investigated seven different inputs that are M1 (Band 1, 2, 3 and 4), M2 (RVI, NDVI and SAVI), M3 (Band 1, 2 and 3), M4 (Band 1, 2 and 4), M5 (Band 1, 3 and 4), M6 (Band 2, 3 and 4) and M7 (Band 1, 2, 3, 4, RVI, NDVI and SAVI). The parameters of training process used training rate 0.1 and 0.2; the RMS exit 0.1 and 0.01; and maximum iterations Twenty one models were produced. The accuracies assessment of land cover used confusion matrix to know the overall, producer s and user s accuracies and also the kappa coefficient. The original band (band 1, 2, 3 and 4) as input (M1) produced the best classification accuracy than the other models (M2, M3, M4, M5, M6 and M7). The best model is shown by model M1-3 with input parameters such as training rate 0.1, RMS 0.2, 6000 iterations with overall accuracy of % and kappa of Three classes i.e. Grass, Non-conifer and Built-up are grouped under good classification using M1-3, M1-4, M1-5 and M1-6. Grass is the best predicted. Meanwhile, Conifer, Water and Bush resulted to bad classification. Keywords: land cover, classification, back-propagation neural network, confusion matrix

4 SUMMARY ARIANI ANDAYANI (2008). Land Cover Classification of ALOS Data Using Back-propagation Neural Network Models. Under the supervision of I WAYAN ASTIKA and HARTANTO SANJAYA. Land cover information is vital for supporting decision concerning the management of the environment and for understanding causes and trends of human and natural processes on the earth surface. Therefore, land cover maps are required by several organizations such as governmental agencies and research institutions for a variety application. The extraction of useful information from satellite images, by means of classification, is one of the most important technical problems of remote sensing. The most commonly used classification methods in remote sensing are statistical classification algorithms such as the minimum distance and the maximum likelihood. Although widely used, conventional statistical classification techniques may not always be appropriate for mapping from remotely data. These methods have their restrictions, related particularly to distributional assumptions and to limitations on the input data types. For example, the requirements and assumptions of the maximum likelihood classification, one of the most widely used techniques, are often unsatisfied. First, as a conventional parametric, the data are assumed to be normally distributed. This may often not be the case and there may be significant inter-class differences in the distributions. Second, to define a representative sample on which to derive descriptive statistics (e.g., mean, and variance) upon which the analysis is based a large training sample is required, this runs contrary to a major goal of remote sensing, which involves extrapolation over large areas from limited ground data. In the past decade the artificial neural network approach, theoretically a more sophisticated and robust method of image classification, has been employed in classification applications. The most widely used is the multilayer perceptron (MLP), a feed-forward artificial neural network model although various types of neural network models have been developed. In an MLP, there are three types of layer, each consisting of processing nodes that are fully interconnected to each other, except that there are no interconnections between nodes within the same layer. These layers are known as the input, hidden and output layers, respectively. The input layer nodes correspond to individual data sources, such as bands of imagery. Hidden layers are used for computations, and the values associated with each node are estimated from the sum of the multiplications between input node values and weights of the links connected to that node. The output layer includes a set of codes to represent the classes to be recognized. Assessing the accuracy of land cover maps is required to evaluate the

5 performance of different back-propagation neural network models. There are many reasons for performing an accuracy assessment. These are curiosity - the desire to know how god something is, to increase the quality of the map information by identifying and correcting the sources of errors and to compare various techniques, algorithms, analysts, or interpreter to test that is best. In addition, if the information derived from the remotely sensed data is to be used in some decision-making process, then it is critical that some measure of its quality be known. One of accuracy assessment that frequently used is error matrix (confusion matrix). This matrix provides information for further analysis such as the overall accuracy, as well as errors of inclusion (commission errors) and errors of exclusion (omission error) and kappa statistics. This study investigated the ALOS data (Advanced Land Observing Satellite) for mapping land cover classification a case study in Bogor Botanical Garden West Java. Land cover type discrimination from remote sensing perspective is not easy, particularly when spectral signature may be closely similar between the land cover type, such as roof and bare land, rangeland and cropland, etc. Image transform is an operation that re-expresses the information content of an image or an image set. The most commonly used transformation applied to remotely-sensed images is band rationing. There are two reasons. One is that certain aspects of the shape spectral reflectance curves of different Earth surface cover types can be brought out by rationing. The second is that undesirable effects on the recorded radiances, such as that resulting from variable illumination (and consequently changes in apparent upwelling radiance) caused by variations in topography can be reduced. One of the most common spectral ratios used in the studies of vegetation status is the ratio of the near infrared (NIR) to the equivalent red band value for each pixel location. This ratio exploits the fact that vigorous vegetation reflects strongly in the near infrared and absorbs radiation in the red waveband. The Ratio Vegetation Index (RVI) was one of the earliest such indices used in remote sensing. Similarly, the modulation ratio for NIR and red reflectance is called the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) is a superior vegetation index for low cover environment. The aim of this research is to develop land cover classification method of ALOS data using Back-Propagation Neural Network classifier a case study in Bogor Botanical Garden West Java. The specific objectives are: to investigate the effect of different input parameter to land cover classification result using Back-Propagation Neural Network and to assess accuracy of the classification result generated with different input and to compare accuracies to decide whether one model is superior to another. Bands rationing was used as input together with the original bands. This study investigated seven different inputs that are M1 (Band 1, 2, 3 and 4), M2 (RVI, NDVI and SAVI), M3 (Band 1, 2 and 3), M4 (Band 1, 2 and 4), M5 (Band 1, 3 and 4), M6 (Band 2, 3 and 4) and M7 (Band 1, 2, 3, 4, RVI, NDVI and SAVI). The parameters of training process used training rate 0.1 and 0.2; the RMS exit 0.1 and 0.01; and maximum iterations Twenty one models were produced. The accuracies assessment of land cover used confusion matrix to know the overall,

6 producer s and user s accuracies and also the kappa coefficient. The classification models have been developed using ALOS data and backpropagation neural network classifier. Among several models that have been developed, model M1-3 (band 1, 2, 3 and 4) showed the best performance. The worst result of classification is shown by model M2 (M2-1, M2-2) which use only band rationing (RVI, NDVI and SAVI) and model M3 (M3-1, M3-2) which use only visible band without NIR resulted to poor agreement of classification accuracy. Other models i.e. M4-1, M4-2 (band 1, 2 and 4); M5-1, M5-2 (band 1, 3 and 4); M6-1 (band 2, 3 and 4); and M7-1, M7-2, M7-3, M7-4 (all images) grouped under moderate agreement of classification accuracy. The best model is shown by model M1-3 with input parameters such as training rate 0.1, RMS 0.2, 6000 iterations with overall accuracy of % and kappa of The predicted best class is Grass with producer s accuracy of % and user s accuracy of 98.81% followed by Non-Conifer with producer s accuracy % and user s accuracy % then Built-up with producer s accuracy % and user s accuracy %. The class that predicted worse is Conifer with producer s accuracy 56 % and user s accuracy % followed by Water with producer s accuracy % and user s accuracy then Bush with producer s accuracy % and user s accuracy 58 %. Three classes i.e. Grass, Non-conifer and Built-up can be grouped under strong agreement (good classification) using M1-3, M1-4, M1-5 and M1-6. Grass is the best predicted. The spectral signature of Grass, Non-conifer and Built-up doesn t mix with other objects so that their brightness value are significantly different with other objects. Meanwhile, Conifer, Water and Bush resulted to bad classification. It could happen caused by the brightness value of Conifer is similar with Water and Bush is confused with Non-conifer..

7 Copy right 2008, Bogor Agricultural University Copy right are protected by law, 1. It is prohibited to cite all or part of this thesis without referring to and mentioning the source: a. Citation only permitted for the sake of education, research, scientific writing, critical writing or reviewing scientific problem. b. Citation does not inflict the name and honor of Bogor Agricultural University. 2. It is prohibit to republish and reproduce all part of this thesis without the written permission from Bogor Agricultural University.

8 LAND COVER CLASSIFICATION OF ALOS DATA USING BACK-PROPAGATION NEURAL NETWORK MODELS ARIANI ANDAYANI A Thesis submitted for the degree of Master of Science of Bogor Agricultural University MASTER OF SCIENCE IN INFORMATION TECHNOLOGY FOR NATURAL RESOURCES MANAGEMENT GRADUATE SCHOOL BOGOR AGRICULTURAL UNIVERSITY BOGOR 2008

9 Research Title : Land Cover Classification of ALOS Data Using Backpropagation Neural Network Models Name : Ariani Andayani Student ID : G Study Program : Master of Science in Information Technology for Natural Resource Management Approved by, Advisory Board Dr.Ir. I Wayan Astika, M.Si Supervisor Hartanto Sanjaya, S.Si., M.Sc. Co-Supervisor Endorsed by, Program Coordinator Dean of the Graduate School Dr. Ir. Hartrisari Hardjomidjojo, DEA Prof. Dr. Ir. Khairil A.Notodiputro, MS Date of examination: November 21 st, 2008 Date of graduation:

10 ACKNOWLEDGMENTS First of all I would like to express my thanks and gratitude to Allah SWT, the Most Merciful whom granted my ability and willing to complete the thesis. I would like to give my highly appreciation to Agency for Marine and Fisheries Research - Ministry of Marine Affairs and Fisheries for granting me the fellowship to study in MSc in IT for NRM, IPB, Bogor, Indonesia. I wish to thank to Dr. Ir. I Wayan Astika, M.Si as my supervisor and Hartanto Sanjaya, SSi. M.Sc as my co-supervisor for their guidance, help, idea, comment and constructive criticism during my research and also Dr. Antonius B.Wijanarto as the external examiner for his positive inputs and ideas. I would like to thank to BAKOSURTANAL through Dr. Antonius B. Widjanarto for permission utilizing of ALOS data and trough Habib Subagyo, M.Si that has given me Topographic Map of Bogor (RBI scale 1: 25,000) and also Dr. Ibnu for his positive inputs. I would like to thank to Ir. Berny A. Subki,Dip.Oc for his support throughout my study, Armaiki Yusmur for his kindness which lend out the Global Positioning System, Bang Cacul and Mbak Nani for their support, Bang Ancha for his kindness to request to his friend in ITC for downloading several literatures and also Hendra Yusron and Lalita Naries for helping me to download several literatures. I would like to thank to MIT secretariat and all staff for helping me to arrange the administration, technical and facilities. I would like to thank to all the member of lectures who taught me the importance knowledge for my future. My truthful thank to my friends in MIT IPB for the wonderful student relations we share together. Finally, I thank to Muhammad Faiq, you know, this thesis could have been nothing without you.

11 CURRICULUM VITAE Ariani Andayani was born in Brebes, Central Java Indonesia on December 8th She was graduated from Gadjah Mada University, Geography Faculty, and Cartography and Remote Sensing Department in From the year of 2001 to 2002, she worked for PT. Exsa International and year 2002 to 2003 worked for PT. Earthline. Since 2003 to present, she works as researcher in Agency for Marine and Fisheries Research - Ministry of Marine Affairs and Fisheries. She received a scholarship from Agency for Marine and Fisheries Research to pursue her graduate study in Master of Sciences in Information Technology for Natural Resources Management in August Her final thesis is Land Cover Classification of ALOS Data Using Back-propagation Neural Network Models

12 External Examiner: Dr. Antonius B.Wijanarto

13 TABLE OF CONTENTS Page LIST OF FIGURES... xiii LIST OF TABLES... xiv LIST OF APENDICHES... xvi I. INTRODUCTION Background Problem Definition Objectives Output... 5 II. LITERATURE REVIEW Remote Sensing for Land Cover Classification Digital Image Classification Method Image Transforms The Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer Type 2 (AVNIR-2) Back-propagation Neural Network Classification Method Artificial Neural Network Multilayer Feedforward Back-propagation Neural Network Model Back-propagation Learning Algorithm Previous Related Research Classification Accuracy Assessment III. RESEARCH METHODOLOGY Time and Location Available Satellite Data and Field Work Data Remote Sensing Data Field Survey Data Required Tools Research Step xi

14 3.4.1 ALOS Image Pre-processing Image Rationing Classification Using Back-propagation Neural Network Comparison IV. RESULT AND DISCUSSION Corrected Imagery Field Survey The Data Set Training Accuracy Assessment: Overall Accuracy Comparing the Overall Accuracies of Input Comparing the Overall Accuracies of ANN Training Comparing the Accuracies of Individual Land Cover Classes Land Cover Maps V. CONCLUSION AND RECOMMENDATION Conclusion Recommendation REFERENCES APPENDICES xii

15 LIST OF FIGURES Page Figure 2-1 The instrument of ALOS; PALSAR, PRISM and AVNIR Figure 2-2 Satellite track of ALOS - AVNIR Figure 2-3 An MLFF network modified for back-propagation (Patterson 1996) Figure 2-4 Back-propagation neural network with one hidden layer (Petterson 1996) Figure 3-1 The location of study area Figure 3-2 The flowchart of general methodology Figure 3-3 Scheme of a back-propagation neural network (Foody 2004) Figure 4-1 The ALOS data before and after geometric correction Figure 4-2 GPS position (yellow point) overlaid with ALOS data RGB Figure 4-3 Image appearance compared with field survey Figure 4-4 The distribution of training sample Figure 4-5 The distribution of reference for accuracy assessment Figure 4-6 The spectral profile of three objects Figure 4-7 The training progress M1-5, M7-1 and M Figure 4-8 The training progress of M1-6, M7-2 and M Figure 4-9 The training progress after iterations Figure 4-10 Relation between RMS and Kappa of all models Figure 4-11 Relation between RMS and Kappa of M Figure 4-12 Land cover maps of model M1-2, M1-3, M1-4, M1-5, M1-6 and M Figure 4-13 Land cover map of Bogor Botanical Garden with false color composite image xiii

16 LIST OF TABLES Page Table 2-1 AVNIR-2 characteristics (EORC JAXA) Table 2-2 Product processing definition (EORC JAXA) Table 2-3 Mathematical example of an error matrix Table 2-4 Example of an error matrix Table 3-1 List of hardware and software Table 3-2 The inputs of classifications Table 3-3 Scenario for classifications Table 3-4 The example of data training for class of non-conifer Table 4-1 The min. and max. value of each band of one scene of ALOS data Table 4-2 Ground control point for geometric correction Table 4-3 The description of each class Table 4-4 The training sample Table 4-5 The reference for accuracy assessment Table 4-6 The statistic of study area Table 4-7 Characteristics and performance of model M1 (Band 1, 2, 3 and 4) Table 4-8 Characteristics and performance of model M2 (RVI, NDVI and SAVI) Table 4-9 Characteristics and performance of model M3 (Band 1, 2 and 3) Table 4-10 Characteristics and performance of model M4 (Band 1, 2 and 4) Table 4-11 Characteristics and performance of model M5 (Band 1, 3 and 4) Table 4-12 Characteristics and performance of model M6 (Band 2, 3 and 4) Table 4-13 Characteristics and performance of model M7 (Band 1, 2, 3, 4, RVI, NDVI and SAVI) Table 4-14 The accuracy of individual classes for M Table 4-15 The accuracy of individual classes for M Table 4-16 The accuracy of individual classes for M Table 4-17 The accuracy of individual classes for M Table 4-18 The accuracy of individual classes for M Table 4-19 The accuracy of individual classes for M xiv

17 Table 4-20 The area (pixels) of each class Table 4-21 The area (percent) of each class xv

18 LIST OF APENDICHES No. Caption Page Appendix 1. The Metadata of ALOS/AVNIR Appendix 2. The data training of each class of sample xvi