基于LVQ神经网络分层模型的遥感影像分类
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摘要
遥感是二十世纪60年代发展起来的一门对地观测综合性技术。随着其飞速发展,遥感技术所具有的时效性强、覆盖范围广、信息丰富客观等优点逐渐被人们所认可,它已经成为人们获取地球表面信息最有效的手段之一。各种遥感系统,主动的和被动的,航天的和航空的,可见光的、红外的和微波的等,为人们提供了越来越多的遥感信息。相比之下,目前遥感信息的处理水平远远滞后于遥感信息的获取水平,这严重制约了遥感技术的应用。
遥感影像分类作为遥感信息处理的一项关键技术,已经广泛应用于土地利用/覆盖分类,取得了良好的社会和经济效益。在一些地表情况复杂的区域,如本研究区所在的腾格里沙漠南缘,由于地物类型多样、地形变化显著、覆盖范围广等特点,导致同物异谱和同谱异物现象十分严重,单纯利用光谱特征的遥感影像土地利用/覆盖分类往往达不到理想的精度,直接影响了遥感影像的应用水平和实用价值。因此,提高遥感影像的分类精度是遥感应用的一个关键问题。
在分类过程中集成光谱特征和辅助特征来提高分类精度已成为一种趋势。有两种方式可以将辅助特征集成到遥感影像分类过程中:其一是把辅助特征作为分类的逻辑通道;其二是利用辅助特征对影像进行地理分区。对于第一种方法,广泛应用于遥感影像分类中的基于统计的分类算法是以参数估计和统计假设为前提,需要分类数据服从一定的分布,而辅助特征各类别数据的分布难以满足这个条件,因而导致分类精度不高。第二种方法需要从辅助特征获取知识来进行分类,遗憾的是这种知识由于表达方式的缺陷而往往带有主观性和不完整性。作为一种分类算法,无需参数估计和统计假设的人工神经网络方法在集成处理光谱特征和辅助特征时具有优势。该文介绍了学习向量量化神经网络,以它为分类器建立一种分层分类模型,依据这个模型,以腾格里沙漠南缘的TM遥感影像为基本数据,在知识的指导下加入形状、位置、纹理等辅助特征进行分类。作为对比,同时进行了单纯利用光谱特征的最大似然法分类。
该文主要研究工作和成果如下:
(1)系统回顾了遥感影像各种常用分类算法的原理与局限性,研究了学习向量量化神经网络的基本原理及其在遥感影像分类中的应用,阐述了它所具有的算法优势,并提出了以它为分类器的分层分类模型。
(2)根据野外考察和知识推理,确定了几种能指示地物分布的辅助特征,通过各类别数据在特征空间中的分布分析,说明了辅助特征在遥感影像分类中的作用,对这些辅助特征进行了量化处理,加入到分类过程中。
(3)利用MATLAB软件的神经网络工具箱设计LVQ神经网络分类器,以研究区的TM影像光谱数据和辅助数据作为分类特征进行区域土地利用/覆盖分类。
(4)分类结果精度评价表明:基于这种模型的分类精度达到了79.5%,实现了较高精度的计算机自动分类。作为对比,同时进行的利用光谱特征的最大似然法分类精度只有73.2%。这表明模型可为其他地表情况复杂区域的土地利用/覆盖遥感分类提供借鉴和参考。
The remote sensing technology, which emerged in 1960s and had been promoted by Physics, Informatics, Geography, has following characteristics: (1) Effectiveness. (2) Large scope. (3) The Integration quality of data. As one major technique for people to observe information of the surface of the earth, remote sensing has become more and more important for our lives and our social. Unfortunately, our ability for abstracting information from remotely sensed imagery still largely lags behind technical developments, so it limits the application of remote sensing.
The classification of remotely sensed imagery is one of the important research content in process of remotely sensed imagery. It has been widely applied in the research on LULC classification. However, a common problem when classifying remotely sensed imagery in order to map land use/cover is the uncertainty in the process of classification: different land use/cover classes present similar spectral signatures and the same land use/cover classes present dissimilar spectral signatures. Because of these, it is very difficult for traditional spectrum-based classification to meet the demand of precision. Therefore, to improve remotely sensed imagery classification accuracy is one of the main topics in the field of the remote sensing research.
In many cases, ancillary characteristics can provide classification with useful information. Also, many attempts have been made to use ancillary characteristics for classification. Ancillary characteristics are incorporated into classification by two approaches: (1) Ancillary characteristics, regarded as logical channels, are input into classification together with spectrum characteristics; (2) The whole image is divided into separate small area based on ancillary characteristics. The two methods above have shortcomings. Compared with other classification algorithms, normal distribution model is not needed for artificial neural networks. In this paper, LVQ neural network was introduced, consequently, a hierarchical classification model based on it was built. The advantages of this model for combining spectral and ancillary characteristics are that it significantly enhances the exploitation of the information. With remotely sensed imagery as the basic data source, this paper used southern margin of Tengle desert as the study area for classification according to the model.
The major works and conclusions are summarized as following:
1. Three major kinds of supervised classification methods are systematically summarized. This paper focuses on the principle and correlative conceptions about LVQ neural network, and a hierarchical classification model based on it was built.
2. Several ancillary characteristics are put forward according to detailed field investigation and knowledge inference. This paper tries to build up the quantitative index of the ancillary characteristics. They were appended to the process of classification.
3. The neural network toolbox of MATLAB was used to design and train the program of neural network, under the environment of MATLAB, TM was classified with the aid of the ancillary characteristics.
4. In comparison with spectral classification based on MLH, this procedure allowed a statistically significant increase of accuracy of land use/cover classification (from 73.2% to 79.5%). experimental results show that this model can provide reference for land use/cover classification under complex topographical conditions.
遥感影像分类作为遥感信息处理的一项关键技术,已经广泛应用于土地利用/覆盖分类,取得了良好的社会和经济效益。在一些地表情况复杂的区域,如本研究区所在的腾格里沙漠南缘,由于地物类型多样、地形变化显著、覆盖范围广等特点,导致同物异谱和同谱异物现象十分严重,单纯利用光谱特征的遥感影像土地利用/覆盖分类往往达不到理想的精度,直接影响了遥感影像的应用水平和实用价值。因此,提高遥感影像的分类精度是遥感应用的一个关键问题。
在分类过程中集成光谱特征和辅助特征来提高分类精度已成为一种趋势。有两种方式可以将辅助特征集成到遥感影像分类过程中:其一是把辅助特征作为分类的逻辑通道;其二是利用辅助特征对影像进行地理分区。对于第一种方法,广泛应用于遥感影像分类中的基于统计的分类算法是以参数估计和统计假设为前提,需要分类数据服从一定的分布,而辅助特征各类别数据的分布难以满足这个条件,因而导致分类精度不高。第二种方法需要从辅助特征获取知识来进行分类,遗憾的是这种知识由于表达方式的缺陷而往往带有主观性和不完整性。作为一种分类算法,无需参数估计和统计假设的人工神经网络方法在集成处理光谱特征和辅助特征时具有优势。该文介绍了学习向量量化神经网络,以它为分类器建立一种分层分类模型,依据这个模型,以腾格里沙漠南缘的TM遥感影像为基本数据,在知识的指导下加入形状、位置、纹理等辅助特征进行分类。作为对比,同时进行了单纯利用光谱特征的最大似然法分类。
该文主要研究工作和成果如下:
(1)系统回顾了遥感影像各种常用分类算法的原理与局限性,研究了学习向量量化神经网络的基本原理及其在遥感影像分类中的应用,阐述了它所具有的算法优势,并提出了以它为分类器的分层分类模型。
(2)根据野外考察和知识推理,确定了几种能指示地物分布的辅助特征,通过各类别数据在特征空间中的分布分析,说明了辅助特征在遥感影像分类中的作用,对这些辅助特征进行了量化处理,加入到分类过程中。
(3)利用MATLAB软件的神经网络工具箱设计LVQ神经网络分类器,以研究区的TM影像光谱数据和辅助数据作为分类特征进行区域土地利用/覆盖分类。
(4)分类结果精度评价表明:基于这种模型的分类精度达到了79.5%,实现了较高精度的计算机自动分类。作为对比,同时进行的利用光谱特征的最大似然法分类精度只有73.2%。这表明模型可为其他地表情况复杂区域的土地利用/覆盖遥感分类提供借鉴和参考。
The remote sensing technology, which emerged in 1960s and had been promoted by Physics, Informatics, Geography, has following characteristics: (1) Effectiveness. (2) Large scope. (3) The Integration quality of data. As one major technique for people to observe information of the surface of the earth, remote sensing has become more and more important for our lives and our social. Unfortunately, our ability for abstracting information from remotely sensed imagery still largely lags behind technical developments, so it limits the application of remote sensing.
The classification of remotely sensed imagery is one of the important research content in process of remotely sensed imagery. It has been widely applied in the research on LULC classification. However, a common problem when classifying remotely sensed imagery in order to map land use/cover is the uncertainty in the process of classification: different land use/cover classes present similar spectral signatures and the same land use/cover classes present dissimilar spectral signatures. Because of these, it is very difficult for traditional spectrum-based classification to meet the demand of precision. Therefore, to improve remotely sensed imagery classification accuracy is one of the main topics in the field of the remote sensing research.
In many cases, ancillary characteristics can provide classification with useful information. Also, many attempts have been made to use ancillary characteristics for classification. Ancillary characteristics are incorporated into classification by two approaches: (1) Ancillary characteristics, regarded as logical channels, are input into classification together with spectrum characteristics; (2) The whole image is divided into separate small area based on ancillary characteristics. The two methods above have shortcomings. Compared with other classification algorithms, normal distribution model is not needed for artificial neural networks. In this paper, LVQ neural network was introduced, consequently, a hierarchical classification model based on it was built. The advantages of this model for combining spectral and ancillary characteristics are that it significantly enhances the exploitation of the information. With remotely sensed imagery as the basic data source, this paper used southern margin of Tengle desert as the study area for classification according to the model.
The major works and conclusions are summarized as following:
1. Three major kinds of supervised classification methods are systematically summarized. This paper focuses on the principle and correlative conceptions about LVQ neural network, and a hierarchical classification model based on it was built.
2. Several ancillary characteristics are put forward according to detailed field investigation and knowledge inference. This paper tries to build up the quantitative index of the ancillary characteristics. They were appended to the process of classification.
3. The neural network toolbox of MATLAB was used to design and train the program of neural network, under the environment of MATLAB, TM was classified with the aid of the ancillary characteristics.
4. In comparison with spectral classification based on MLH, this procedure allowed a statistically significant increase of accuracy of land use/cover classification (from 73.2% to 79.5%). experimental results show that this model can provide reference for land use/cover classification under complex topographical conditions.
引文
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