published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / abstract / Geography / Master / Master of Philosophy

published_or_final_version / abstract / Geography / Doctoral / Doctor of Philosophy

published_or_final_version / Urban Studies / Master / Master of Social Sciences

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Geography and Geology / Doctoral / Doctor of Philosophy

published_or_final_version / Geography and Geology / Master / Master of Arts

Chan, King-Chong. / Thesis (M.Phil.) -- Chinese University of Hong Kong, 1991. / Bibliography: leaves 150-163. / Abstract --- p.i / Acknowledgements --- p.ii / Figures --- p.vii / Tables --- p.x / Chapter Chapter 1 --- Introduction --- p.i / Chapter 1.1 --- Background --- p.2 / Chapter 1.2 --- Objectives --- p.5 / Chapter 1.3 --- Hypotheses --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.6 / Chapter Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Land Use and Land Cover --- p.10 / Chapter 2.2 --- Informational Classes and Spectra I Classes --- p.11 / Chapter 2.3 --- Simple Per-Pixel Classification Method --- p.12 / Chapter 2.4 --- Scene Noise and Boundary Effect --- p.14 / Chapter 2.5 --- Using Filtered Data --- p.16 / Chapter 2 .6 --- Textura1 Classifier --- p.18 / Chapter 2.7 --- Contextual Classifier --- p.22 / Chapter 2.8 --- Geographic Information System (GIS) --- p.24 / Chapter 2.9 --- Expert System and Artificial Intelligence (AI) --- p.25 / Chapter 2.10 --- Concluding Remarks --- p.27 / Chapter Chapter 3 --- Methodology --- p.30 / Chapter 3.1 --- Spectral Class Composition Method (SCCM) --- p.32 / Chapter 3.1.1 --- The Concept of the Spectral Class Composition Method --- p.32 / Chapter 3.1.2 --- Unsupervised Classification Process --- p.39 / Chapter 3.1.3 --- Training Process --- p.39 / Chapter 3.1.4 --- Proportion Counting --- p.40 / Chapter 3.1.5 --- Number of Spectral Class --- p.41 / Chapter 3.1.6 --- Window Size --- p.42 / Chapter 3.1.7 --- Transect Process --- p.43 / Chapter 3.1.8 --- Classification Task --- p.45 / Chapter 3.1.9 --- Summary --- p.47 / Chapter 3.2 --- Research Design --- p.49 / Chapter 3.2.1 --- Study Area --- p.49 / Chapter 3.2.2 --- Data and Instruments Used --- p.51 / Chapter 3.2.3 --- C1assification Scheme --- p.51 / Chapter 3.2.4 --- Accuracy Assessment --- p.52 / Chapter Chapter 4 --- Results and Discussion I--- Examining the Relationship Between Land Use and Spectral Classes --- p.55 / Chapter 4. 1 --- Unsupervised Classification --- p.57 / Chapter 4.1.1 --- Unsupervised Classification Process --- p.57 / Chapter 4.1.2 --- Unsupervised Classification Results --- p.58 / Chapter 4.1.3 --- Difference Between Spectral Class Maps --- p.65 / Chapter 4.2 --- Training Process --- p.68 / Chapter 4.2.1 --- Definition of Training Process --- p.68 / Chapter 4.2.2 --- Selection of Training Sites --- p.69 / Chapter 4.2.3 --- Spectral Class Composition Data Extracted from the Training Sites --- p.70 / Chapter 4.2.4 --- Spectral Heterogeneous Characteristics of Land Use Types --- p.73 / Chapter 4.2.5 --- Different Number of Spectral Classes --- p.77 / Chapter 4.2.6 --- Similar Composition Results in Some Land Use Types --- p.80 / Chapter 4.2.7 --- Using Spectra1 Class Composition Data as Rules of Classification --- p.81 / Chapter 4.3 --- Proportion Counting --- p.83 / Chapter 4.3.1 --- Window-Based Proportion Counting Process --- p.83 / Chapter 4.3.2 --- Transect Process --- p.85 / Chapter 4.3.3 --- Variation of Spectra I Class Proportion within a Land Use Type --- p.91 / Chapter 4.3.4 --- Variation of Spectral Class Proportion among Land Use Types --- p.95 / Chapter 4.4 --- Summary --- p.103 / Chapter Chapter 5 --- Resu1ts and Discussion II --- Classification and Accuracy Assessment --- p.104 / Chapter 5.1 --- Rule-Based Land Use Classification --- p.106 / Chapter 5.1.1 --- Derivation of Rules for Classification --- p.106 / Chapter 5.1.2 --- Using Rules for Classification --- p.106 / Chapter 5.1.3 --- Modification of the Rules --- p.109 / Chapter 5.1.4 --- C1assification Resu11s --- p.109 / Chapter 5.2 --- Accuracy Assessment --- p.118 / Chapter 5.2.1 --- Accuracy Assessment Process --- p.118 / Chapter 5.2.2 --- Analysis of Error Matrices --- p.123 / Chapter 5.2.3 --- Comparison Between Spectral Class Composition Method and Simple Per-Pixel Method --- p.126 / Chapter 5.2.4 --- Discussion on the Resui.ts of Producer's and User's Accuracy --- p.130 / Chapter 5.2.5 --- Discussion on Number of Spectral Classes --- p.132 / Chapter 5.2.6 --- Discuss i on on Window Size --- p.134 / Chapter 5 .3 --- Summary --- p.136 / Chapter Chapter 6 --- Conclusion --- p.138 / Chapter 6.1 --- Summary --- p.139 / Chapter 6.2 --- Limitations and Problems --- p.142 / Chapter 6.3 --- Contribution --- p.147 / Chapter 6.4 --- Further Research --- p.148 / Bibliography --- p.150