運用影像頻率域特徵建構景觀偏好預測模型之研究

Using Image Features from the Frequency Domain to Construct a Prediction Model of Landscape Preference

This research establishes a prediction model of landscape preference using spatial envelope theory and provides a new perspective and novel tool for landscape research. The 480 images used in this research comprised eight types of scenes, including highways, tall buildings, streets, inner cities, coasts, forests, the countryside, and mountains. Principal component analysis extracted three important features-spatial texture, spatial direction, and spatial depth-from the frequency domain of these images, which were used to construct the prediction model of the landscape preference. The results show that the spatial texture could predict the preference for inner-cities, while spatial direction and spatial depth could predict the preference for roads and forests. Although future research could improve the prediction ability of this model, special envelope theory provides a new direction for landscape research.