Mr. Miaomiao Cheng Profile

Miaomiao Cheng

at Zhejiang Forestry Univ

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Proceedings Article | 30 October 2009 Paper

Extraction of the urban green space based on the high resolution remote sensing image

Miaomiao Cheng, Hong Jiang, Jian Chen, Zheng Guo

Proceedings Volume 7498, 749813 (2009) https://doi.org/10.1117/12.832786

KEYWORDS: Vegetation, Earth observing sensors, High resolution satellite images, Spatial resolution, Image classification, Statistical analysis, Near infrared, Satellites, Image resolution, Remote sensing

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High resolution image can be used to distinguish the small difference of the ground things. Texture information can avoid the matter of same spectral from different objective and different spectral with same objective which must be faced when making classification with only spectral information. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation in urban area. A high spatial resolution IKONOS image, coincident field data covering the urban area of linping scenic region in Yuhang town, Zhejiang province in china, was used in this analysis. The vegetation of test region was classified as tea garden, masson pine, fir, broadleaves, and shrub/herb based on the field data. Semi-variograms were calculated to differentiate vegetation classes and assess which window sizes were most appropriate for calculation of grey-level co-occurrence texture measures. The texture analysis showed that co-occurrence mean, variance, contrast, and correlation texture measures provided the most significant statistical differentiation between vegetation classes. Subsequently, a decision tree classification was applied to spectral and textural transformations of the IKONOS image data to classify the vegetation. Using both spectral and textural image bands yielded the good classification accuracy (overall accuracy=81.72%). The results showed that it has the higher accuracy to extract the urban green space from IKONOS imagery with the spectral and texture information, as well as the vegetation index.

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