Study on extraction of urban green space from IKONOS remote sensing images
IKONOS影像在城市绿地提取中的应用

This paper discusses about the extraction of urban green space from an IKONOS image using a hierarchical classification technique. Green space information was obtained based on the spectral characteristics of different objects with the help of available corresponding methods after the combination of IKONOS multi-spectral data. Due to high resolution of IKONOS imagery, large amount of data and heterogeneous nature of spectrum, the extraction of urban green space was carried out on segments after image segmentation. This would help much improving the accuracy of extraction of urban green space from the whole image. In test area of the image, the spectral characteristics of different features in all 4 bands are analyzed. The spectral characteristics of old urban area and asphalt road are similar to those of part of green space. Moreover, it is difficult to extract green space under the shadow. In order to extract information from the mixed green space with non-green space, through enhancing NDVI values of a green space under the shadow, parts of green space are extracted (NDVI > 0.18), then parts of non-green space are eliminated. The next step is to extract green space from mixed green space and non-green space based on spectral knowledge and unsupervised ISODATA clustering. Finally, green space information of test area is obtained by aggregating different levels of green space. The methodology is basically concerned with the object spectral features and noise due to the mixture of different land-use/land-cover categories is significantly avoided. To demonstrate the efficiency of proposed method, unsupervised ISODATA clustering method was used to extract green space from the test area,then both results were compared to show accuracy. The visual interpretation and ground truth checks of the test area have proved that the classification accuracy and productivity accuracy of the first method are higher than that of the latter.