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 CLAS