英文文献 科技类 原文及译文33

Multi-texture-model for Water Extraction Based on Remote Sensing Image
Hua WANG, Li PAN, Hong ZHENG
School of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,
Wuhan 430079,P.R.China
School of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.China
Abstract:
In this paper, a multi-texture-model for water extraction based on remote sensing imagery is proposed. The model is applied to extract inland water (including wide river, lake and reservoir)from high-resolution panchromatic images. Firstly directional variance is used to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.
1. Introduction
The recognition of water from remote sensing image has drawn considerable attention in recent yeas. A large number of publications about water extraction appeared and various approaches for water extraction have been proposed. Zhou developed a descriptive model for automatic extraction of water based on spectral characteristics[1]. Barton applied channel 4 for NOAA/AVHRR to extract water[2]. Du proposed a approach for water extraction from SPOT-5 based on decision tree algorithm[3]. Li recognized and monitored clear water from MODIS[4]. Wu extracted water from Quick Bird image and used active contour model to obtain accurate position of river bank[5]; In order to extract water from high-spatial remote sensing images, He used wavelet technique to expend the information and cleaned main noise of the images, and then presented multi-window linearity reserve technique to conserve linear water[6].
Recently, most research work on water extraction was forced on automatic recognition of water from remote sensing images based on spectral characteristics. However, there are some disadvantages of these methods: (1) The resolution of image used for water extraction is low. The minimum size of recognizable object is depended on the spatial resolution of sensor. Therefore it is difficult to obtain accurate position of water boundary. (2) Due to the characteristic of water itself and the sensor applied, in certain channels the spectral features of different objects are equilibrated. The equilibration leads to the phenomena of “different objects same image” or“different images same object”, which results in noise objects included in extraction result.
In this paper, a multi-texture-model for water extraction based on remote sensing is proposed. The model is applied to extract inland water (including wide river, lake and reservoir) from
high-resolution panchromatic image. Firstly directional variance is applied to find river regions, and then grain table is adopted to avoid noise including objects that have similar directional variance characteristic as water surfaces. The experiment result shows that the proposed method provides an effective way for water extraction.
This paper is organized as follows. In Section 2, the directional variance model adopted is introduced. Then, fusion of proposed grain table model with directional variance model is discussed in Section 3.The experimental results of the proposed multi-texture-model and comparative studies with single models are given in Section 4. We conclude this paper in Section 5.
2. Directional Variance Model
The aim of our research is to extract water larger than 100m2from panchromatic images. As shown in Figure 2(a), the research objects can be divided into three classes: wide river, lake and reservoir, which all represent as region in high-resolution imageries. The objects of background can be divided into two classes: building and cropland, which also represent as region.In panchromatic imagery, wide river has a similar gray level to building and cropland, though the mean gray
of lake and reservoir is much lower than the background objects. Conventional methods for water extraction based on spectral characteristics are not effective in the situation. In the meantime, water body defines homogeneous areas whereas building and cropland correspond to heterogeneous regions. Therefore, we take into account the homogeneity of the image to separate wide river, lake and reservoir from background instead. To characterize the difference of homogeneity between water body and the other types of areas, we use a textual operator: the directional variance.
2.1. The Directional Variance Operator
The operator is derived from those defined by Guerin & Maitre and Airault & Jamet[10]. As shown in Figure1, the directional variance consists in computing, for each pixel M of the image, the variance of the gray levels of the image on several direction of a circle whose center is M and radius is R. Then, the direction with the highest variance value is kept. Its direction defines the direction for which image is the most heterogeneous, locally. Its variance value is the directional variance value of the pixel M.
2.2. Extraction of water based on directional variance
According to the definition of the operator, the minimum acreage of recognizable water body is depended on the length of radius R. We have chosen a length of 10 pixels for 1m resolution. The directional variances of the five typical training samples (wide river, lake, reservoir, building and cropland) have been computed and the statistical comparison is summarized in Table1. The overall average of water directional variance is lower than the objects of background.Nevertheless, the directional variance of cropland is similar to wide river with overlapping potion over 90%.In
high-resolution panchromatic imagery, details inside wide river, such as boat, wave, etc, are represented clearly which result in the heterogeneous of water. In the meantime, the textures of parts of building (for example, roof ) and cropland are rather fine. In a small window, these potions define homogeneous areas with similar directional variance as wide river. The result is improved if we chosen a length of 100 pixels. The statistical comparison is shown in Table2. If the length of radius is large enough, directional variance of building is higher than other objects obviously with no overlapping portion; the difference between cropland and wide river is increased while the overlapping potion is decreased. However, increasing the radius leads to two problems which are outlined as follow:
1) The size of recognizable water body increases;therefore water which has small acreage (for example narrow river) can not be detected.
2) The position of water bank is not accurate although the spatial resolution of imagery is rather high.
Hence, in this paper, a multi-texture-model is presented and two texture models are fused to extract water from panchromatic images. Firstly, we chose a radius of 10 pixels to extract water based on directional variance; and then, grain table is adopted to avoid noise including parts of building and cropland that have similar directional variance characteristic as water surface.
3. Multi-texture-model
In high-resolution imagery, cropland and building represents structural characteristic. According to this characteristic, grain analysis is adopted for further research on the original extraction based on directional variance. The grain table histogram is able to represent structural characteristic of the research object, which can be applied to recognize many kinds of different objects [12].
3.1. Extraction of water fused by grain table
The grain table histograms of the five typical training samples (wide river, lake, reservoir, building and cropland) are computed and correlation coefficients between them are summarized in Table3. Correlation coefficients between water classes are over 85%, however, correlation coefficients between water classes and background classes are lower than 65%.Hence, we compare the correlation coefficients of regions in extraction image base on directional variance with three water samples and two background samples respectively. If the region has a higher correlation coefficient with background classes, it will be marked background and wiped off[13].
4.Experimental Results
We run the algorithm on several high-resolution panchromatic images. In Figure2.(a), we have been considering an aerial photograph(6126×4800) of a region in Wuhan, China, the resolution of which is 1m,including building, cropland, wide river( Changjiang river), lake, reservoir and cropland. The results of extraction based on directional variance with radius of 10 pixels is displayed in Figure2.(b), and clearly, water has been detected completely, whereas parts of building and cropland are included as noise objects in the result. Water extraction using directional variance with radius of 100 pixels is displayed in Figure2.(c)with correctness over 95%, however, small lakes are missed and the position of bank is not as accurate as Figure2.(b). Finally, in Figure2.(d), the result of Figure2.(b) is fused by grain table analysis, so that the correctness and completeness of extraction are both over 90%.
5. Conclusions
Based on textural analysis of water in high-resolution panchromatic imagery, a multi-texture-model is presented for water extraction.The experimental results proved that the approach is efficient for inland water (including wide river, lake and reservoir) extraction. As the complexity and diversity of water, the rate of recognition of our algorithm fluctuates. Furthermore, the method is supervised which needs a lot of human interference to obtain training samples. Therefore, there are problems to be solved in future:
1) Our further work should be extensible to multispectral remote sensing images.
2) To decrease human interference, old vector will be applied to obtain training samples instead. 6. Acknowledgments
The work was supported by the National Key Technology R&D Program of China under grant No.2006BAB10B01.
根据遥感图象的多纹理模型相关的水抽取
Hua WANG, Li PAN, Hong ZHENG
School of Remote Sensing and Information & Engineering, Wuhan University 129 Luoyu Road,
Wuhan 430079,P.R.China
School of Electronic Information, Wuhan University 129 Luoyu Road, Wuhan 430079,P.R.China
文摘：
在本文中，提议了一个多纹理模型为根据遥感成像的水提取。

而且运用模型从高分辨率泛色图象提取内陆水域(包括宽河、湖和水库)。

首先定向变化用于发现河地区，五谷桌然后被采
取避免噪声包括有相似的定向变化特征当水表面的对象。

实验结果表示，提出的方法为水提取提供有效方式。

1．简介
在最近这些年，水的图像测量识别是作为一个相当值得关注的问题。

有大量的关于水的抽提的出版物涌现，以及关与水的抽提的多种方法被大量的提出。

周朝曾经发展过一个基于光谱特征的描述型的自动抽取模型。

巴顿在NOAA/AVHRR应用下用来抽取液体。

Du被提议于从SPOT-5基于决策树运算法则来逐步进行水的抽取。

Li认可了并且从MODIS监测出了清楚的水。

Wu利用从飞的很快的鸟的图象中抽取水的流体特性并运用运动等高模型来获取河岸的准确位置。

为了从高空间遥感图象提取水的特性，他使用小波技术扩展信息并且消除了图象的主要噪声，然后利用被提出的多窗口线性预留技术保存线性水。

最近，在水抽取的多数研究工作是牵强的在水的自动识别从根据光谱特性的遥感图象的过程。

然而，有这些方法的有些缺点：(1) 用于水抽取的图象的决议是低的。

可辩识的对象的极小的大小取决于传感器的空间分辨率。

所以获得水边界限的准确位置是难的。

(2) 由于水自身的特征和传感器被用于在某些渠道应用的不同的对象的特点被均衡化。

这种平衡带来了“不同的对象现象同样图象”或“不同的图象同样对象”的现象，导致在提取结果包括了噪声对象。

在本文，一个基于细微的感觉的水抽取的多纹理模型被最终提议。

运用模型从高分辨率泛色图象提取内陆水域(包括宽河、湖和水库)的范围内进行。

首先定向变化的趋势被应用于发现河地区，然后颗粒表格的方法被采取于避免噪声包括有相似的定向变化特征当水表面的对象。

实验结果表示，这些被提出的方法都很好的为水提取提供有效方式。

本文接下来要讨论的问题有：在第2部分，将介绍被采取的定向变化模型。

然后，在部分3会提出的颗粒表格法模型的融合与定向变化模型在提出的多纹理模型.其中实验性结果会被谈论，并且在第4部分将给出比较研究同唯一的模型。

最后，我们在第5部分结束本文。

2．定向变化模型
我们的研究的目标大于100m2from泛色图象将提取水。

如图2 (a)所显示，研究对象可以被划分成三类：宽河、湖和水库，所有在高分辨率成像都会被代表作为区域。

背景对象可以被划分成二类：大厦和农田，也将会代表作为区域。

在泛色成像，宽河有相似的灰级级别的图像，并且农田，这个背景对象可能会比湖和水库的灰度低。

常规方法为：根据光谱特性的水抽取是不是有效的情况下所决定。

同时，水本体也定义了同类的区域，而修造和农田则可以对应于各种不同的地区。

所以，我们考虑到图象的同质性则从背景上改为分离成宽河、湖和水库。

要描绘同质性区别在水本身和其他区域类型之间的不同，我们使用一个本文里讲到操作：定向变化。

2.1. 定向变化的操作
这些操作的过程都是源于从Guerin& Maitre和Airault & Jamet定义的那些结论中找到的。

如Figure1所显示，定向变化在计算其中救包括为图象，图象的灰级的变化的每个映像点M 都是以m为圆心，并且半径是R的弧线的几个不同指导方向上进行的。

然后，方向以最高的变化价值被保留。

它的方向当地定义了图象是最异向的方向。

它的变化价值就是映像点M.的定向变化价值。

2.2. 基于定向变化理论上的抽取水过程
根据操作流程的定义，可辩识的水本体的极小的面积也取决于半径R的长度。

我们选择了10个映像点的长度为1m决议。

五个典型的测试样品(宽河、湖、水库、大厦和农田)的定向变化将会在Table1被计算了和统计来进行比较总结。

整体平均水定向变化将有可能低于背景对象。

然而，农田的定向变化对于宽河是相似的，并且重叠的部分超过90%。

在高分辨率泛色导致不同种的水的成像，细节在宽河里面显示出来，例如小船、波浪等等，都可以清楚
地表示出来。

同时，大厦的部分纹理(例如，屋顶)和农田是相当完整的。

在一个小窗口里，这些部分定义了同类的区域以相似的定向转变成宽河。

这个改进结果就是如我们选上的100个映像点的长度。

统计的比较已经在Table2显示了。

如果半径的长度足够大的时候，大厦的定向变化其他对象明显地高于其他没有重叠的部分; 而与农田和宽河之间的区别，当重叠的部分被减少时，相应的也会增加。

然而，增加半径导致被概述和跟随的会产生二个问题：
1) 可认识的水本体增量的大小; 例如可能有一些有面积的水(狭窄的河)不可能被查出。

2) 岸边的位置不是准确的，虽然成像的空间分辨率可能还是相当高。

因此，在本文，多纹理模型被提出，并且二个纹理模型被熔化从泛色图象提取水。

首先，我们选择半径10个映像点是基于定向变化抽取水；然后，颗粒表格模型
被采取避免噪声包括有相似的定向变化特征作为水表面的相关大厦和农田的部分。

3. 多纹理模型
在高分辨率成像的领域，农田和大厦代表了大部分结构特征。

根据这个特征，颗粒分析将会为对根据定向变化的原始的提取的数据进一步研究所采取而进行下去。

颗粒表格模型直方图就是能代表研究对象的结构特征，并且可以被应用认可于许多不同种类的对象。

3.1. 由颗粒表格所解决的水的抽取模型
在表格3中五个典型的测试样品(宽河、湖、水库、大厦和农田)的颗粒表格模型直方图将会被计算，并且在他们之间作相关系数的总结。

在水类之间相关系数为85%，然而，在水类和背景类之间相关系数将有可能低于65%。

因此，我们在提取图象基数范围内的相关系数将会在定向变化与各自三个水样已经两个背景样品之间作比较。

如果在此范围内有一个更高的相关系数与背景类，那么它将是被标记的背景和抹去。

4. 实验的结果
我们在几个高分辨率泛色图象运用算法进行运算。

在Figure2.(a)，我们在中国的武汉，决议将被设为一张空中相片(6126×4800)的一个区域，并且决定用1m来作为标准，包括大厦，宽河(长江)，湖、农田水库和农田。

根据定向变化的提取的结果与半径10个映像点在Figure2.(b)被显示，和清楚地表示水完全地被查出了，而大厦和农田的部分包括当噪声对象在结果。

水提取使用定向变化
与半径100个映像点在Figure2(c)中被显示。

并且以正确性95%为标准，然而，小湖将会背错过，并且银行的位置不是一样准确的象Figure2.(b)。

终于，在Figure2.(d)，结果的Figure2.(b)由颗粒表格分析所解决，因此提取的正确性和完整性两个90%。

5. 结论
基于对水的质地分析在高分辨率泛色成像，多纹理模型为水抽取被提出。

实验性结果证明，方法为内陆水域(包括宽河、湖和水库)提取是高效率的。

作为水的复杂性和变化性，我们的算法得到了公认的率动摇。

此外，需要很多人的干涉得到训练样品的方法被监督。

所以，下面列出有今后将要解决的问题：
1) 我们的进一步工作应该是延伸性的到多光谱遥感图象。

2) 要减少人的干涉，将运用住得一些老传播媒介改进而得到新的测试样品。

6. 鸣谢
在津贴No.2006BAB10B01之下的由中国的全国重要技术R&D节目支持的工作。