集成夜间灯光数据与Landsat TM影像的不透水面自动提取方法研究

Automatic Extraction Method for Impervious Surface Area by Integrating Nighttime light Data and Landsat TM Images

利用多源遥感数据提取不透水面信息是一个重要的研究方向。针对以往研究中多需要人工选取不透水面样本进行模型训练的问题,本文通过整合夜间灯光遥感与Landsat TM影像中的空间和光谱信息实现了不透水面覆盖范围(Impervious Surface Area,ISA)的自动提取。首先根据夜间灯光的分布来定位ISA聚集的城市区域的位置,分别在城市区域内部和外部自动提取可靠性高的ISA及非ISA样本,然后通过迭代分类提取城市区域的ISA,再以此为样本对城市区域外部进行分类,最后将分类结果整合完成整幅影像的ISA提取流程。应用本方法对美国雪城地区的DMSP/OLS夜间灯光影像上提取了84个城市区域,提取精度大于95%。从中分别选择高ISA密度和低ISA密度的2个城市区域作为ISA提取的测试区,本文方法在城市区域内的ISA提取总体精度与kappa系数分别为88.23%和0.63;在城市区域外部为78.6%和0.54,均优于人工样本选取方法的提取精度,表明该方法能够实现精度稳定且高效的ISA自动提取。

Using multi-source remote sensing data to extract impervious surface information is an important and active research direction. The present study integrated spatial and spectral information from nighttime light data and Landsat TM remote sensing images to automatically extract the coverage information of Impervious Surface Area （ISA）, given that in the previous studies, manual selection of impervious surface samples was usually needed for model training. In the present method, firstly, ISA concentrated urban areas were located according to the distribution of nighttime lights. Thus, the ISA spectral characteristics of the local scale in the urban area parts were more clear and obvious compared to the whole-image scene scale. Meanwhile, for the urban exterior, there were mostly non-ISA pixels, therefore the soil samples which were easily confused with ISA were extracted from the urban exterior, and the general spectral features of these samples on this image were calculated. These features could be utilized to distinguish ISA pixels from urban areas. Thus, highly reliable ISA and non-ISA samples were automatically selected from urban area and urban exterior, respectively. Secondly, ISA from urban areas was extracted by an iterative classification process. For the iterative classification process, new samples from the previous extraction results were collected and then added to the following classification process, to make the features of the ISA samples more representative of different types of ISA coverage. Then, ISA samples of urban area were selected from the extraction results, combined with the non-ISA samples of the urban exterior. A sample set was formed to classify the urban exterior. Lastly, the classification results were integrated to complete the whole image. An experiment with this method was completed. DMSP/OLS nighttime light images and Landsat5 TM images of the Syracuse, USA were chosed. 84 urban areas were extracted and the detection accuracy rate was above 95% compared to the Openstreet map. Two urban areas with high and low ISA density from the detection results were selected as the test areas. Automatic selection of ISA and non-ISA samples were performed to the TC transform feature bands of the Landsat5 TM images. The overall accuracy and kappa coefficients of sample selection in urban areas were 92.45% and 0.76, respectively, and 96.52% and 0.85 in urban exterior. For the results extracted by decision tree classifier, the average overall accuracy and Kappa coefficient were 88.23% and 0.63 in the urban areas; 78.6% and 0.54 in the urban exterior. These results are superior to manual methods. This is because the approach of automatic samples selection was more capable of obtaining samples of mixed pixel types compared to manual samples selection. Moreover, the representativeness of samples in spatial distribution and spectral characteristics was better since the iterative classification process was introduced. It suggests an automated classifcaltion workflow is achieved by the proposed method, and this method is reliable and effective for both urban area and urban exterior. In further researches, it could be expected that the ISA extraction accuracy could be improved by optimizing classification characteristics （e.g. adding space features） and improving classification algorithms.