Comparison Bn Supervised&Unsupervised Neural Networks Senait D Senay 2003

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Centre for Geo-Information Thesis Report GIRS-2003-08

A Comparison Assessment Between Supervised and Unsupervised Neural Network Image Classifiers

Author: Senait Dereje Senay

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January 2003

Supervisor: Dr. Monica Wachowicz

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WAGENINGEN UR

Center for Geo-Information Thesis Report GIRS-2003-08

A Comparison Assessment Between Supervised and Unsupervised Neural Network Image ClassifiersSenait Dereje Senay

Thesis submitted in the partial fulfillment of the degree of Master of Science in Geoinformation Science at the Wageningen University and Research Center

Supervisor: Dr. Monica Wachowicz

Examiners: Dr. Monica Wachowicz DRS. A.J.W de Wit Dr. Ir. Ron van Lammeren January 2003 Wageningen University

Center for Geo-Information and Remote Sensing Department of Environmental Sciencesii

To my parents: Lt.Col. Yeshihareg Chernet and Ato Dereje Senay, And my brother: Daniel Dereje Thank you for everything you have been to me.

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Acknowledgements

I am indebted to my supervisor Dr. Monica Wachowicz, who gave me continuous professional support during all stages of undertaking the thesis. I would like to sincerely thank her for the invaluable advice and support she gave me. I am very grateful for Dr. Gerrit Epema, and Dr. Ir. Ron Van Lammeren who helped me in facilitating the field trip to the study area for ground control point collection and of course for the continuous moral support I have got from Dr. Gerrit Epema. I would sincerely like to thank Dr. Gete Zeleke and Mr. Meneberu Allebachew, who helped me by facilitating vehicle and other necessary data and support while I went to the study area for field data collection; without their help the field trip would not have been successful at all. I would also like to express my gratitude for Mr. Wubshet Haile and Mr. Getachew, who assisted me throughout the field work, enabling me to finish the field work with in a very limited time I had. I would like to extend my heart felt thanks to Mr. John Stuiver and Drs. Harm Bartholomeus, who supported me whenever I needed a professional held in preprocessing of data; without their support the data preprocessing stage of my thesis would definitely have taken more time. I would also like to thank the cartography section of Alterra who helped me in printing and scanning maps used in producing the report as well as in the analysis. I would like to extend my heartfelt thanks to my friends Achilleas Psomas () and Krzysztof Kozowski (Dzikuj), for all the friendly moral support, and invaluable friendship; thanks for making my stressful days easier. I gratefully thank Dawit Girma, for all the help I got whenever I needed it. I would also like to show my gratefulness to my uncle Mr. Tesfasilassie Senay for providing me a family atmosphere while I went for a fieldtrip. Yet I would not pass without expressing my gratitude and sincere thanks to my friends, Giuseppe Amatulli (Grazie), Mauricio Labrador-garcia and Sonia Barranco-Borja (Gracias), Nicolas Dosselaere (Dank u wel), Izabela Witkowska (Dzikuj), Fanus Woldetsion (Yekenyeley), Adrian Ducos (Merci) for creating a pleasant working atmosphere, and much more, which helped us during the difficult times of working on the thesis, and which is also unforgettable. , I wish you all the best in the future. Last but not least, I would like to extend my admiration to the whole GRS 2001 batch for the respect and friendship between us I wish you all the best, nothing but the best. It has been an honor and pleasure to know you. Finally, I would like to extend my heartfelt thanks for NUFFIC for covering my study costs and offering me this experience.

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Abstract

Neural networks are a recently emerged science, which developed as part of artificial intelligence. They are used in solving complex problems in various disciplines. The application of neural networks in remote sensing particularly in image classification has become very popular in the last decade. The motivation to use neural networks arose due to the limitations in using the conventional parametric image classifiers, as the source, data structure, scale and amount of remotely sensed data became highly varied. Fortunately neural networks are found to compensate the drawbacks; these conventional classifiers have towards image classification. Neural networks offer two kinds of image classification, supervised and unsupervised. In this study both neural networks were tested to evaluate, which will result in a better accuracy image classification and which method handle poor quality data better. Finally, a land cover map of southern part of Lake Tana area situated in North West part of Ethiopia is produced from the best classifier.

Key words: Neural Networks, neuron ANN, KWTA, LVQ, BP, Image classification

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Abbreviations

ANN ASTER BP KWTA LVQ MIR MLNFF MLP NN NDVI SOFM SWIR TIR VNIR WTA

Artificial Neural Networks Advanced Spaceborne Thermal Emission and Reflection Radiometer Back Propagation Kohonens Winner Take All Learning Vector Quantization Middle Infrared Multi-Layer Normal Feed Forward Multi-Layer Perceptron Neural networks Normalized Difference Vegetation Index Self-Organizing Feature Maps Short Wave Infrared Thermal Infrared Visible and Near Infrared Winner-Take All

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Table of ContentsAcknowledgements .......................................................................................................... iv Abstract.............................................................................................................................. v Abbreviations ................................................................................................................... vi List of Figures................................................................................................................... ix List of Tables ..................................................................................................................... x 1 Introduction .................................................................................................................... 1 1.1 BACK GROUND ........................................................................................................... 1 1.2 STUDY AREA .............................................................................................................. 3 1.3 OBJECTIVES................................................................................................................ 5 1.4 RESEARCH QUESTIONS ............................................................................................... 5 1.5 RESEARCH OUTLINE................................................................................................... 5 2 Artificial Neural Networks (ANN) ............................................................................... 6 2.1 OVERVIEW OF THE MAIN CONCEPTS ............................................................................ 6 2.1.1 Biological concepts ............................................................................................ 6 2.1.2 Historical development ...................................................................................... 7 2.1.3 Basic neural network processor ........................................................................ 9 2.1.4 Neural networks and image classification ....................................................... 11 2.2 TYPES OF NEURAL NETWORKS .................................................................................. 12 2.2.1 Supervised neural network classifiers ............................................................. 13 2.2.1.1 Description of supervised neural network classifiers ............................... 13 2.2.1.2 Architecture and algorithm ....................................................................... 14 2.2.2 Unsupervised neural networks classifiers ....................................................... 18 2.2.2.1 Description of unsupervised Neural Network classifiers.......................... 18 2.2.2.2 Architecture and algorithm ....................................................................... 19 3 Methodology ................................................................................................................. 24 3.1 FIELD DATA ACQUISITION......................................................................................... 26 3.2DATA PREPROCESSING .............................................................................................. 27 3.2.1 Datasets ........................................................................................................... 27 3.2.1.1 ASTER ...................................................................................................... 27 vii

3.2.1.2 Landsat TM ............................................................................................... 29 3.2.2 Datasets preparation ....................................................................................... 30 3.2.3 Training and test sets preparation ................................................................... 32 3.3 SUPERVISED NEURAL NETWORK CLASSIFICATION .................................................... 34 3.4 UNSUPERVISED NEURAL NETWORKS CLASSIFICATION .............................................. 35 3.5 ACCURACY ASSESSMENT AND VALIDATION ............................................................. 37 3.6 SENSITIVITY ANALYSIS............................................................................................. 37 3.7 IMPLEMENTATION ASPECTS ..................................................................................... 37 4 Results and Discussion................................................................................................. 39 4.1 ACCURACY OF BACK PROPAGATION CLASSIFIER TRAINED WITH ASTER OR LANDSAT TM DATASETS................................................................................................................ 39 4.2 ACCURACY OF BACK PROPAGATION CLASSIFIER TRAINED WITH ASTER AND LANDSAT TM INPUT DATASET ....................................................................................... 40 4.3 ACCURACY OF KOHONEN/LVQ CLASSIFIER TRAINED WITH ASTER OR LANDSATDATASETS ...................................................................................................................... 41

4.4 ACCURACY OF KOHONEN/LVQ CLASSIFIER TRAINED WITH ASTER AND LANDSAT TM COMBINED DATASETS .............................................................................................. 42 4.5 VALIDATION OF THE RESULTS OBTAINED FROM THE BACK PROPAGATION SUPERVISEDNEURAL NETWORKS CLASSIFIER ..................................................................................... 43

4.6 VALIDATION OF THE RESULTS OBTAINED FROM THE KOHONEN/LVQ UNSUPERVISEDNEURAL NETWORK CLASSIFIER....................................................................................... 45

4.7 IMPROVING THE TRAINING DATA QUALITY ............................................................... 46 4.8 SENSITIVITY ANALYSIS ............................................................................................ 49 5 Conclusions ................................................................................................................... 51 6 Recommendation.......................................................................................................... 55 References ........................................................................................................................ 56 Appendices ....................................................................................................................... 59 APPENDIX1: DATASET PROJECTION INFORMATION ........................................................ 59 APPENDIX2: RESULTS OF INPUT SENSITIVITY ANALYSIS ............................................... 60 APPENDIX3: NEURAL NETWORK PARAMETERS USED...................................................... 62

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List of FiguresFigure 1: Map of Ethiopia ................................................................................................... 4 Figure 2. Overview of the study area .................................................................................. 4 Figure 3: Signal path of a single human neuron ................................................................. 6 Figure 4: The basic neural networks processor; the neuron, and its functions. .................. 9 Figure 5: Design of the Multi-layer Feed Forward (MLNFF) architecture ...................... 14 Figure 6: A Kohonen Self Organizing Grid - 2 Dimensional Output Layer .................... 19 Figure 7: Decreasing neighborhood of a winner neuron in a WTA output layer. ............ 21 Figure 8: Design of Learning Vector Quantitzation Architecture .................................... 22 Figure 9: Overview of the main procedures involved in the methodological process...... 25 Figure 10: ASTER bands superimposed on model Atmosphere. ..................................... 28 Figure 11: Landsat TM bands superimposed on model Atmosphere. .............................. 29 Figure 12: Study area after Lake Tana is masked out of the image. ................................. 30 Figure 13: Spectral signature of the six classes (before ASTER image rescaling) ......... 31 Figure 14: Spectral signature of the six classes (After ASTER image rescaling) ........... 31 Figure 15: Training/test data preparation procedure......................................................... 32 Figure 16: Design of Back Propagation Neural Network ................................................. 34 Figure 17: The design of the KWTA/LVQ network......................................................... 36

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List of TablesTable 1: Spectral range of bands and spatial resolution for the ASTER sensor ............... 28 Table 2: Spectral range of bands and spatial resolution for the TM sensor ...................... 29 Table 3: Training and test sets for ASTER dataset. .......................................................... 33 Table 4: Training and test sets for Landsat TM dataset .................................................... 33 Table 5: Training and test sets for the combination of ASTER and Landsat TM datasets33 Table 6. Training data set up for the Back Propagation neural network .......................... 35 Table 7. Training data set up for the Kohonen Winner Take All/LVQ network .............. 36 Table 8: Accuracy of the back propagation classifier using ASTER data ........................ 39 Table 9: Accuracy of the back propagation classifier trained with Landsat TM data ...... 40 Table 10: Accuracy of the back propagation classifier trained with ASTER and Landsat TM combined datasets. ............................................................................................. 41 Table 11: Accuracy of the Kohonen/LVQ classifier trained with ASTER data ....

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