一种提取不透水面的可见光波段遥感指数

为了从高分辨率无人机影像中获取准确的城市不透水面信息，在可见光波段范围内建立绿-蓝光谱特征空间，综合土壤线及不透水面线，构造了能够将土壤、植被像元与不透水面像元有效分离的绿-蓝不透水面指数。以广州市局部地区的GF-2号影像为验证数据对比及分析垂直不透水层指数、比值居民地指数以及绿-蓝不透水面指数的提取结果，以验证绿-蓝不透水面指数的可行性与提取精度。同时，将眉山市洪雅县部分地区的无人机正射影像作为试验数据进行不透水面提取。结果表明，在3个不透水面提取指数的横向对比中，绿-蓝不透水面指数和垂直不透水层指数的提取结果总体精度相同，验证了绿-蓝不透水面指数的有效性。在对无人机正射影像的不透水面提取中，得益于无人机低空摄影技术能够获取地形特征的特点，解决了建筑物屋顶因植被覆盖导致的错分问题，提取结果总体精度达到了96.95%，Kappa系数为0.936 1。试验证明了绿-蓝不透水面指数能够代替归一化差值不透水面指数、垂直不透水层指数、比值居民地指数等，应用于无人机遥感影像的不透水面信息提取中。

A visible-band remote sensing index for extracting impervious surfaces

Unmanned Aerial Vehicle (UAV) remote sensing can obtain high-resolution images at low cost and high efficiency. However, there is rare research on the use of UAV remote sensing images to extract information from impervious surfaces. The difficulty of the research lies in that for the high-resolution UAV red-green-blue images, there is currently not an exclusive impervious surface index that can be applied to the extraction of impervious surface information. To address this problem, this study established the green-blue spectral feature space in the blue and green bands of the visible-bands. Under this spectral feature space, the green-blue impervious surface index was proposed to effectively separate soil-vegetation pixels and impervious surface pixels. The process of constructing the index was as follows. Firstly, the feature points were divided into impervious surface points and pervious surface points in the green-blue spectral feature space. Secondly, the least-squares fitting was conducted on the impervious surface points and the previous surface points. Then, the impervious surface line and the soil line were obtained, and a reference line was constructed between the two straight lines. Finally, the distance from the feature point to the reference line was used as the expression of the index. To verify the accuracy of the difference between the green-blue impervious surface index and other impervious surface indexes applied to satellite remote sensing images, comparison and analysis were performed on the extraction of the impervious surface by perpendicular impervious index, ratio resident-area index and green-blue impervious surface index. At the same time, the experiment was conducted using the UAV orthophoto image data of Hongya county in Meishan city to extract the impervious surface. The analysis was conducted to investigate the effect of the green-blue impervious surface index on the extraction of the impervious surface information in the UAV remote sensing image. The experimental results showed that: 1) The green-blue impervious surface index constructed the blue and green bands as the feature space had the same accuracy as the perpendicular impervious index and ratio resident-area index based on the blue and near-infrared bands in terms of the impervious surface extraction, and the overall accuracy reached over 94%. 2) The green-blue impervious surface index showed strong applicability and replaced the indexes like perpendicular impervious index, ratio resident-area index and biophysical composition index in the images lacking near-infrared, mid-infrared, and thermal-infrared bands. The green-blue impervious surface index was used as a remote sensing index to extract visible-bands on the impervious surfaces. 3) As a visible light wave impervious surface extraction index used in UAV remote sensing, the green-blue impervious surface index could not only effectively distinguish between soil and impervious surfaces, but also utilized the characteristics of easy-to-obtain terrain features by UAV remote sensing. For the problem of misclassification, the overall accuracy of the extraction results by the green-blue impervious surface index reached 96.95%, and the Kappa coefficient was 0.936 1. The green-blue impervious surface index constructed based on the green-blue spectral feature space effectively separated soil pixels and extracted high-precision urban impervious surfaces from UAV remote sensing images. For satellite imagery, the existing impervious surface index had a good performance in extracting the surfaces, while the proposed green-blue impervious surface index was more suitable for UAV remote sensing. At present, extracting urban impervious surfaces from UAV remote sensing images has gradually become an important application.