基于高分辨率遥感影像的南京典型城区绿地信息提取
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摘要
本研究是高分辨率遥感影像在城市绿地监测与制图中的具体应用,同时验证了高分数据在该应用中的有效性。本文以江苏省南京市为研究区,利用高分辨率QuickBird遥感影像,对传统的影像分类方法进行改进,精确地提取了城市绿地的类型和面积。研究内容主要分为三个方面：遥感影像中阴影的去除、NDVI与阈值法结合提取植被信息、以及GEMI(?)旨数的改进。
遥感数据在城市植被制图中的应用已有数十年的历史。最近,随着各种诸如QuickBird的高分辨率遥感影像越来越容易获得,更高分辨率的城市土地利用制图也获得了发展。高分辨率卫星遥感影像将以它的高时效和低成本等特性更好的服务于各个规划部门。准确和细致的市区土地利用信息对许多市政活动,如城市土地管理、城市规划、城市景观类型分析、环境研究等,都有着重要的作用,因此,城市绿地信息的精确提取有着十分重要的实际意义。本文主要研究内容和结论如下：
(1)遥感影像中阴影区域的检测和去除。
如何解决阴影问题是数字图像处理过程的难点之一。遥感图像中阴影是由于光源发出的直射光完全或部分被阻塞产生的,其中包括高层物体的投影和该物体本身投射在地面上的阴影。阴影对遥感影像的影响一直以来就是遥感研究中的一个重要问题。由于被遮挡物体光谱信息的减少或者完全损失,对这些对象的分类和解释就面临着巨大的困难。阴影问题对城市环境的高空间分辨率遥感影像的分析尤为重要。建筑物,桥梁,高塔和树木等较高的地物都在很大程度上增强了阴影效果。遥感影像中的阴影检测和去除技术正在进一步发展。阴影检测是确定遥感影像中阴影像元的过程,而阴影的去除则是一个恢复阴影区域的光谱信息以重新获得无阴影图像的过程。
(2)结合归一化植被指数(NDVI)和域值法提取城市绿地的种类和面积。
首先对遥感影像进行处理,计算出影像的NDVI,并提取出NDVI值大于0.036的象元。这些象元中的大部分属于植被,但范围并没有包含所有的城市植被。为了得到所有的植被象元,我们将NDVI阂值降低至-0.2,但是在这种情况下,一些在红光与近红外波段与植被光谱特征相似的蓝色和白色金属屋顶会被错分为植被。
为了纠正这种错分现象,我们将蓝色屋顶、白色屋顶和植被在QuickBird影像四个波段上的光谱特征进行了对比,发现植被在蓝光波段的像元值小于380,我们可利用这点从这三中错分地物中分离出植被。
另外,杉树与水体和人造草皮操场的光谱存在相似的特征,所以在提取出的NDVI>-0.2并且蓝光波段<380的象元中,仍然存在错分现象。为了解决这个问题,我们对比了这三种地物的光谱信息。经过对比得出通过限定红光波段的值在200到300之间可以从这三种地物中识别出杉树。
(3)提出一种改进的GEMI指数,提高了分类的精度。
通过对遥感影像进行处理,得出影像的GEMI指数。基于GEMI指数可以较好的提取出植被,但由于植被和蓝色屋顶的GEMI旨数比较相似,存在错分现象。为了消除植被和蓝色屋顶的错分现象,我们对GEMI指数进行了改进,并加入了条件蓝光波段<380,这种新的方法称作改进的GEMI指数(MGEMI).在城市绿地信息提取中,通过MGEMI旨数的应用,蓝色屋顶带来的影响基本上得以消除。最后,本文对城市绿地监测与制图中NDV、GEMI、MGEMI三种指数的效果进行了对比和分析。
This study is an application of high-resolution remotely sensored data in extracting urban green landscape. In particular, the capabilities of the data are assessed. The study area was in Nanjing, Jiangsu Province, China. The type and area of urban green land was extracted finely for the high resolution of QuickBird image. An improved traditional classification method was developed to extract the urban vegetation more accurately.
●QuickBird image and unsurprised classification methods were used to detect the shadow of remotely sensed imagery and enhanced infrared value helped to extract the vegetation pixel under the shadow.
●NDVI and spectrums value were combined to extract urban vegetation, including the vegetation type and area.
●GEMI was developed to improve the accuracy of extracting the urban vegetation, and the new equation called Modified Global Environment Monitoring Index.
Remotely sensed data have been used for urban land cover mapping for decades. The recent availability of high spatial resolution imagery from satellite sensors such as QuickBird provides new opportunities for detailed urban land cover mapping at very fine scales. The low and decreasing cost of High Resolution (HR) Remotely Sensed satellite imagery such as QuickBird may motivate planning departments, saving considerable time and money.
Accurate and detailed land cover information of urban areas is essential for many purposes such as urban land management, urban planning, and urban landscape pattern analysis, environmental study, and others. Thus, the accurate and detailed extraction of urban green land is of great significance. The research content and results were as follows
(1) Detecting the shadows and recovering the vegetation under shadow.
The problem of shadowing is a challenge raised in digital image processing. Shadows in a remotely sensed imagery occur when objects totally or partially occlude the direct light from a source of illumination, which include shadows cast on the ground feature by high-rise objects, and self shadows. The effect of shadows in remote sensoring has been an important issue. Great difficulty arises in classification and interpretation of shaded objects in an image because of the reduction or total loss of spectral information of those shaded objects.
In the monitoring of urban vegetation, the problem of shadowing is particularly significant in high spatial resolution imagery. With the dominance of elevated objects such as buildings, bridges, towers and trees in the landscape, the proportion of the imagery that is affected by shadowing could be relatively large.
Shadow detection and removal were investigated in remotely sensed imagery. Shadow detection is the process of identifying the shaded pixels in remotely sensed imagery, in the study unsupervised classification used to detect the shadowing area, whereas a new algorithm applied for restoration shadow and recover the vegetation pixels, the radiometric enhancement methods increased the value of infrared spectrum to be and add to the unsupervised classification image to detected the missing vegetation under the high building shadow.
The new application algorithm is the value of Unsupervised classification+(08*Infrared).With raster attribute editor shows the vegetation pixels as trees under the shadow. Shadow removal is used to restore the spectral information of the shaded areas to obtain a shadow-free image.
(2) Extract urban vegetation used NDVI and spectrums value.
When we derived NDVI and got a NDVI value greater than0.036, a huge number of the pixels marking the vegetation is seen, and it contained the majority of vegetation. After collecting the fraction of the remaining vegetation we get a NDVI value greater than-0.2, but in this case the extracted vegetation cover also included some blue and white metal roofs, which have similar spectral characteristics to vegetation in the red and near-infrared band.
To remove this influence of the roofs, the investigation focused on the value of the three features, blue roofs, white metal roofs, and vegetations in the four bands of QuickBird image. The vegetation value in the blue band shows it should be less than380.
There was still a problem to extract the pine fir trees which have the similar spectral characteristics to water and artificial playgrounds. There was still a problem to extract the pine fir trees after the application of NDVI>-0.2and Blue band<380. But when we have the value for these three features, we can recognize the pine fir trees in the red band with the value between200and300.
(3) Developing Global Environmental Monitoring Index (GEMI) to
improve the accuracy of extraction of urban vegetation
Global Environmental Monitoring Index (GEMI) has been applied in the study area. It gives good results, but also creates some confusion because of the similar spectral characteristics of blue roofs and vegetation.
Global Environmental Monitoring Index (GEMI) has been modified to eliminate and remove the blue roofs influence, which have similar spectral characteristics to vegetation, the factor of blue band as Blue band<380added to (GEMI) equation, then the new equation called Modified Global Environmental Monitoring Index (MGEMI). After applying the MGEMI and obtaining the preliminary green space extraction, it seems that most of the effects of the blue roofs have been eliminated. The use of Modified Global Environment Monitoring Index (MGEMI) was compared to the Global Environment Monitoring Index (GEMI) and NDVI in this research for extracting urban green land and was found to be more accurate.
遥感数据在城市植被制图中的应用已有数十年的历史。最近,随着各种诸如QuickBird的高分辨率遥感影像越来越容易获得,更高分辨率的城市土地利用制图也获得了发展。高分辨率卫星遥感影像将以它的高时效和低成本等特性更好的服务于各个规划部门。准确和细致的市区土地利用信息对许多市政活动,如城市土地管理、城市规划、城市景观类型分析、环境研究等,都有着重要的作用,因此,城市绿地信息的精确提取有着十分重要的实际意义。本文主要研究内容和结论如下：
(1)遥感影像中阴影区域的检测和去除。
如何解决阴影问题是数字图像处理过程的难点之一。遥感图像中阴影是由于光源发出的直射光完全或部分被阻塞产生的,其中包括高层物体的投影和该物体本身投射在地面上的阴影。阴影对遥感影像的影响一直以来就是遥感研究中的一个重要问题。由于被遮挡物体光谱信息的减少或者完全损失,对这些对象的分类和解释就面临着巨大的困难。阴影问题对城市环境的高空间分辨率遥感影像的分析尤为重要。建筑物,桥梁,高塔和树木等较高的地物都在很大程度上增强了阴影效果。遥感影像中的阴影检测和去除技术正在进一步发展。阴影检测是确定遥感影像中阴影像元的过程,而阴影的去除则是一个恢复阴影区域的光谱信息以重新获得无阴影图像的过程。
(2)结合归一化植被指数(NDVI)和域值法提取城市绿地的种类和面积。
首先对遥感影像进行处理,计算出影像的NDVI,并提取出NDVI值大于0.036的象元。这些象元中的大部分属于植被,但范围并没有包含所有的城市植被。为了得到所有的植被象元,我们将NDVI阂值降低至-0.2,但是在这种情况下,一些在红光与近红外波段与植被光谱特征相似的蓝色和白色金属屋顶会被错分为植被。
为了纠正这种错分现象,我们将蓝色屋顶、白色屋顶和植被在QuickBird影像四个波段上的光谱特征进行了对比,发现植被在蓝光波段的像元值小于380,我们可利用这点从这三中错分地物中分离出植被。
另外,杉树与水体和人造草皮操场的光谱存在相似的特征,所以在提取出的NDVI>-0.2并且蓝光波段<380的象元中,仍然存在错分现象。为了解决这个问题,我们对比了这三种地物的光谱信息。经过对比得出通过限定红光波段的值在200到300之间可以从这三种地物中识别出杉树。
(3)提出一种改进的GEMI指数,提高了分类的精度。
通过对遥感影像进行处理,得出影像的GEMI指数。基于GEMI指数可以较好的提取出植被,但由于植被和蓝色屋顶的GEMI旨数比较相似,存在错分现象。为了消除植被和蓝色屋顶的错分现象,我们对GEMI指数进行了改进,并加入了条件蓝光波段<380,这种新的方法称作改进的GEMI指数(MGEMI).在城市绿地信息提取中,通过MGEMI旨数的应用,蓝色屋顶带来的影响基本上得以消除。最后,本文对城市绿地监测与制图中NDV、GEMI、MGEMI三种指数的效果进行了对比和分析。
This study is an application of high-resolution remotely sensored data in extracting urban green landscape. In particular, the capabilities of the data are assessed. The study area was in Nanjing, Jiangsu Province, China. The type and area of urban green land was extracted finely for the high resolution of QuickBird image. An improved traditional classification method was developed to extract the urban vegetation more accurately.
●QuickBird image and unsurprised classification methods were used to detect the shadow of remotely sensed imagery and enhanced infrared value helped to extract the vegetation pixel under the shadow.
●NDVI and spectrums value were combined to extract urban vegetation, including the vegetation type and area.
●GEMI was developed to improve the accuracy of extracting the urban vegetation, and the new equation called Modified Global Environment Monitoring Index.
Remotely sensed data have been used for urban land cover mapping for decades. The recent availability of high spatial resolution imagery from satellite sensors such as QuickBird provides new opportunities for detailed urban land cover mapping at very fine scales. The low and decreasing cost of High Resolution (HR) Remotely Sensed satellite imagery such as QuickBird may motivate planning departments, saving considerable time and money.
Accurate and detailed land cover information of urban areas is essential for many purposes such as urban land management, urban planning, and urban landscape pattern analysis, environmental study, and others. Thus, the accurate and detailed extraction of urban green land is of great significance. The research content and results were as follows
(1) Detecting the shadows and recovering the vegetation under shadow.
The problem of shadowing is a challenge raised in digital image processing. Shadows in a remotely sensed imagery occur when objects totally or partially occlude the direct light from a source of illumination, which include shadows cast on the ground feature by high-rise objects, and self shadows. The effect of shadows in remote sensoring has been an important issue. Great difficulty arises in classification and interpretation of shaded objects in an image because of the reduction or total loss of spectral information of those shaded objects.
In the monitoring of urban vegetation, the problem of shadowing is particularly significant in high spatial resolution imagery. With the dominance of elevated objects such as buildings, bridges, towers and trees in the landscape, the proportion of the imagery that is affected by shadowing could be relatively large.
Shadow detection and removal were investigated in remotely sensed imagery. Shadow detection is the process of identifying the shaded pixels in remotely sensed imagery, in the study unsupervised classification used to detect the shadowing area, whereas a new algorithm applied for restoration shadow and recover the vegetation pixels, the radiometric enhancement methods increased the value of infrared spectrum to be and add to the unsupervised classification image to detected the missing vegetation under the high building shadow.
The new application algorithm is the value of Unsupervised classification+(08*Infrared).With raster attribute editor shows the vegetation pixels as trees under the shadow. Shadow removal is used to restore the spectral information of the shaded areas to obtain a shadow-free image.
(2) Extract urban vegetation used NDVI and spectrums value.
When we derived NDVI and got a NDVI value greater than0.036, a huge number of the pixels marking the vegetation is seen, and it contained the majority of vegetation. After collecting the fraction of the remaining vegetation we get a NDVI value greater than-0.2, but in this case the extracted vegetation cover also included some blue and white metal roofs, which have similar spectral characteristics to vegetation in the red and near-infrared band.
To remove this influence of the roofs, the investigation focused on the value of the three features, blue roofs, white metal roofs, and vegetations in the four bands of QuickBird image. The vegetation value in the blue band shows it should be less than380.
There was still a problem to extract the pine fir trees which have the similar spectral characteristics to water and artificial playgrounds. There was still a problem to extract the pine fir trees after the application of NDVI>-0.2and Blue band<380. But when we have the value for these three features, we can recognize the pine fir trees in the red band with the value between200and300.
(3) Developing Global Environmental Monitoring Index (GEMI) to
improve the accuracy of extraction of urban vegetation
Global Environmental Monitoring Index (GEMI) has been applied in the study area. It gives good results, but also creates some confusion because of the similar spectral characteristics of blue roofs and vegetation.
Global Environmental Monitoring Index (GEMI) has been modified to eliminate and remove the blue roofs influence, which have similar spectral characteristics to vegetation, the factor of blue band as Blue band<380added to (GEMI) equation, then the new equation called Modified Global Environmental Monitoring Index (MGEMI). After applying the MGEMI and obtaining the preliminary green space extraction, it seems that most of the effects of the blue roofs have been eliminated. The use of Modified Global Environment Monitoring Index (MGEMI) was compared to the Global Environment Monitoring Index (GEMI) and NDVI in this research for extracting urban green land and was found to be more accurate.
引文
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