Abstract: Street view analysis backed with big data can help landscape architects quickly and effectively evaluate the feelings of the built environment. However, the current street view analysis mainly relies on advanced cognition such as semantic segmentation of image content, which seldom considers the characteristics of the primary processing of human visual perception. Research evidences show that in the primary processing of visual perception, rich high-frequency detailed information can enhance people's preference for the environment and strengthen its recovery effect. To explore the differences in the details of different landscape images, the authors select typical landscape images, including 150 natural landscapes, 69 urban landscapes, 60 forest landscapes with diversified canopy coverage, and 31 Chinese classical garden landscapes, to analyze the Fourier slopes of their high-frequency details. The results show that the seemingly simple natural elements, such as rock mass, sand, water surface, and lawn pruning, are richer and more varied than the details of urban artificial landscapes, and their corresponding Fourier slope is still closer to nature, and the tree canopy details are higher than the typical natural landscapes. In particular, the Fourier slope of Chinese classical gardens is closer to typical nature, presenting richer, more nature-like complex details. This research expands the possible ways to improve the street environment experience. In addition to utilizing natural elements such as plants, simulating the rich and varied details and forms of nature can have the same effect.