| Summary: | 碩士 === 國立交通大學 === 土木工程系所 === 94 === Rice is the most important crop in Taiwan. Its field planting inventory is a routine government operation. The inventory is used to support decisions and policies related to crop yield estimation, hazard mitigation, and other relevant areas. Starting in 1980, the government has been utilizing aerial photo interpretation as the primary inventory method. This procedure is based on manual interpretation. If an automated classification system can be implemented, both the time and cost required for the inventory can be reduced. The errors caused by subjective interpretation of data by humans can also be avoided.
This study investigates the application of the Support Vector Machine (SVM) and Plausible Neural Network (PNN) for image classification. The images used for the experiment include multi-temporal Formosat-2 images of the Chiayi area and multi-temporal SPOT images of the Hsinchu area. Other classification schemes are used for comparison: Gaussian Maximum Likelihood Classification, error Back-Propagation (BP) neural network, Learning Vector Quantization (LVQ) neural network, Radial Basis Function (RBF) neural network, and Rough Set theory. Two implementations of PNN are adopted. V1.3 is the general version, while V 1.0 takes both spatial and attribute relations into account. On the other hand, there are a number of kernel functions to be selected with SVM. The stability of SVM and Maximum Likelihood Classification, with respect to the contribution of texture images and the number of clusters and training samples, in the Chiayi dataset are also evaluated.
PNN V1.0 performs the best for both datasets. The Overall Accuracy is higher than 94% for Chiayi and 91% for Hsinchu. SVM performs second-best for Chiayi and third-best for Hsinchu. Among different Kernel functions, RBF and polynomials are shown to be better than linear and two-layer neural networks. The Polynomial Kernel Function is the best for Chiayi and RBF is the best for Hsinchu. SVM has higher stability than Maximum Likelihood Classification with respect to the number of clusters and training samples. The contribution of texture images for classification is not significant. Instead, the influence of some texture images is found to be negative.
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