基于支持向量机分类的面向对象土地覆被图像分类方法研究
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摘要
面向对象的遥感图像分析是遥感图像处理技术的发展方向之一。面向对象的遥感图像分析方法也称为基于分割单元的分析方法,而对象是指通过一定的图像分割算法对遥感图像进行分割,得到内部属性相对一致或均匀程度较高的图像片段或区域单元。
在土地覆被分类应用领域,这种分割单元就是土地利用斑块。面向对象的分析方法主要特点是它识别和分类的目标是对象片段或区域,而不是单个像元,把这个识别分类过程建立在分割单元层次上的方法叫做面向对象遥感图像分类技术。面向对象分类技术优越点不仅在于充分考虑了地物的光谱特征,而且利用了地物之间的空间信息,因此,已经在一些应用领域得到有效的推广使用。但在面向对象土地覆被遥感图像分类技术中,基于核函数学习理论的分类方法尚待深入研究,特别是基于支持向量机理论的面向对象分类技术研究开发及应用方面缺少全面和深入的基础研究支持。
本文正是基于统计中的核函数学习理论,针对支持向量机在新型土地覆被面向对象遥感图像分类技术研究中亟待解决的理论框架问题与关键技术,依据统计学、计算图像工程学、遥感影像解译、数字图像处理学的相关原理,研究了基于支持向量机理论的面向对象分类技术,探索了支持向量机理论在面向对象问题上的应用潜力、能力和前景。具体开展了以下三个方面的研究工作(研究内容和创新点):
1.开始从广义遥感图像分类的最小单位即像素角度出发,总结了核密度函数在图像分类中已有的研究成果,分析了核密度函数在分类中的显著特点,提出了一个快速“核密度估计Bayes监督分类”土地覆被分类模型,并开发了一个分类子应用系统。
首先因为合理选择核函数是支持向量机分类方法的重要组成部分。其次,了解核函数的带宽参数特点,对于核密度梯度图像分割方法中重要尺度参数选择具有重要意义。核函数使支持向量机可以容易地是实现非线性映射运算。本章通过对经典高斯核函数的改进,初步认识核密度函数的带宽参数的特征及物理意义。研究结果表明:将该快速变换后的核函数应用于遥感图像土地覆被分类,达到了快速计算的目的,分类精度与同等条件下标准支持向量机相接近,实验结果也验证了改进后核函数跟踪样本分布的有效性。
本章的研究作为下一步面向对象分类的必要的基础。
2.以遥感图像核密度梯度图像分割理论为基础,建立在分割单元即对象层次上,逐步开展了基于支持向量机理论的面向对象图像分类技术研究工作和子应用系统开发工作。进行了一系列的面向对象层级基础性研究工作。以下研究方法和相应开发的子应用系统,归类于单一尺度面向对象分类技术( Single Layer Object-Oriented SVM,简记为SOBIA-SVM)体系中。
(1)提出了采用标准支持向量机理论的面向对象遥感图像分类方法,并开发了相应的子系统程序(S-SVM)。提出的建立在分割单元特征基础上的方法S-SVM,充分利用了支持向量机在小样本特征空间中的优良的识别特性。通过统计分析,说明提出的分类方法优于传统KNN方法。实验结果表明,该方法可以应用到遥感图像面向对象识别分类中。
(2)提出了基于模糊支持向量机理论的面向对象分类方法,并开发了相应子系统程序(F-SVM)。提出的采用模糊支持向量机的面向对象模式分类方法(F-SVM),对分割得到的对象单元进行了特征模糊数学权重评价,对特征中的“奇异”向量点进行弱化权重评价,使其对分类超平面的影响最低。通过统计分析,说明提出的分类方法优于未进行样本模糊权重处理的方法。实验结果表明,提出的方法精确度相比标准支持向量机方法提高了5.1%左右。
(3)提出了基于最小二乘支持向量理论的面向对象分类方法,并开发了两个子系统程序S-LSSVM和FG-LSSVM。
首先,对分割单元直接选择特征单元对象,利用标准最小二乘支持向量机进行模型的建立工作,实验数据表明,采用S-LSSVM方法,图像识别率达到了92.8%,对比相同测试样本条件下最近邻KNN方法为95.2%,SVM为95.2%,相差约2.4%左右,另外,直接S-LSSVM面向对象方法计算速度相对更快外,检测精度接近同等条件下S-SVM和KNN面向对象分类模型。
然后,在此S-LSSVM子系统基础上,深入研究并提出采用模糊灰色关联理论对样本单元进行预处理条件下的面向对象分类方法,并开发了相应的程序FG-LSSVM。提出采用最小二乘支持向量机与模糊灰色关联理论特征样本预先处理的新组合分类算法。实现了一个较高精度的遥感图像面向对象分类信息系统。实验结果表明,提出的FG-LSSVM面向对象分类方法相比较标准支持向量机S-SVM、模糊支持向量机F-SVM、以及最近邻KNN面向对象方法,其实验精度提高了2.4%左右,相比未进行特征样本预先处理的S-LSSVM算法,精度提高了约4.8%左右。提出的新方法在识别性能和精度上有了改善外,应用方面也和以上面向对象方法一样符合研究区实际分类应用的要求。
3.引入计算机视觉中的层次识别技术,通过改变遥感图像分割尺度,把大分割单元对象层划分到最底层,并在该层次建立起支持向量机识别模型,从而先行提取出大尺度层次上土地覆被地物来,剩下的小尺度地物单独建立一个层次,并在该层以小尺度参数重新分割图像,并且建立新的支持向量机模型,提取小尺度地物类型。通过计算机视觉中的图像准确掩模编程技术,将各层级提取的地物掩模叠加,得到最后的分类结果。
本章是建立在第二部分的单一尺度面向对象分类研究(SOBIA-SVM)体系基础之上,并重点研究提出一个新的多层次的面向对象方法( Multi-Layer Object-OrientedSVM,简记为MOBIA-SVM),提出了分类理论框架,思路侧重以实用价值和学术意义为出发点,进行了算法设计和应用实验,研究结果表明,提出的多层次支持向量面向对象分类方法,不仅适用大幅面尺度遥感图像分类,尤其是对小尺度地物分类精确度提高显著。此方法作为加快实现实际应用研究提供了基础研究支撑。
结束语本项研究为进一步深化和完善支持向量机理论在面向对象遥感图像分类中的应用,进行了有益的探索并提供了技术支撑,也为土地覆被面向对象分类技术的发展,提供了基础研究参考。同时对计算机视觉技术应用,特别是遥感数字图像分割技术进一步研究,具有重要的实际促进意义。
Object-oriented remote sensing image analysis is one of the technology development of remote sensing images. Object-oriented remote sensing image analysis method, also known as cell-based segmentation method of image analysis, while the object is picked up through a certain image segmentation algorithm for remote sensing image, to get relatively homogeneity within the object, or even higher uniformity degree of image fragments or regional units.
In the application of land cover classification domain, this unit is the land use patch.Object- oriented analysis method of main characteristic is its recognition and classification target is object fragments or regions, rather than a single pixel. We make this identification and classification of process which based on segmentation method of unit level is called the object - oriented remote sensing image classification techniques.
The superiority of object - oriented classification technology is not only to give full con-sideration to the spectral characteristics of the surface features, and take advantage of the spatial information between surface features. Therefore, this technology has been e?ective promotion in many application fields. However, in object-oriented land cover remote sensing image classi-fication techniques, this methodology based on kernel function method of learning theory need to be in-depth study. In particular, the introduction of support vector machine technology, the application of the technique is lack of comprehensive and in-depth basic research support.
The research is focused on as well-known urgently need to be solved for the theoretical problems and key technologies in the new land cover object-oriented technology, which based on kernel learning theory, support vector machine applied research in remote sensing image, according to the related principles of digital image processing, statistics, computing image en-gineering, remote sensing, image interpretation, researching on object - oriented classification based on support vector machine theory, exploring using support vector machine theory on the issue of object - oriented application potential, ability and prospects. Specific research content and its related innovation is carried out as the following three aspects(research contents and some innovations ):
1. Starting from the generalized minimum unit which is as pixels of remote sensing image classification perspective, summed up kernel density function in image classification research results, analyzed some notable feature of kernel density function classification, put forward a rapid“The kernel density estimation of Bayes classification”of land cover classification model and established a classification system. As take the preliminary work of object-oriented analysis for the further research.
First of all, a reasonable choice for the kernel function is an important part to support vector machine approach, and this step is also an important for scale parameter selection in the nuclear density gradient image segmentation method. Kernel function can easily make support vector machine to achieve non-linear algorithms. By classical Gaussian kernel function improvement, an initial understanding is acquired on the bandwidth parameters of the nuclear density function, as well as its characteristics and physical meaning. The results show that: The rapid transformation of nuclear function is applied to remote sensing images of land cover classification to achieve a quick calculation purposes, Classification accuracy under the same conditions with the standard support vector machines are compared, The experimental results also verified the improved kernel function to track the e?ectiveness of the sample.
This section studies as basic research pave the way as next step by using Support Vec-tor Machine technology and the kernel density gradient image segmentation theory of object-oriented classification
2. Based on the theory of kernel density gradient of remote sensing image segmentation,on object level, we step by step carried out classification theory and technology research work and system establishment work based on Support Vector Machines. We carried out a series of object-level basic research. This work is classified in a single-scale object-oriented classifica- tion (SOBIA-SVM).
(1) We provided an object-oriented remote sensing image classification method based on the standard support vector machine theory, and the development of the corresponding sub-program (S-SVM). Proposed methods of cell characteristics of S-SVM which is based on seg-mentation, its full use of excellent identification feature of support vector machine in small sample feature space. Experimental data analysis results show that the method can be applied to the object-oriented high-resolution remote sensing image recognition category. And with the traditional the nearest neighbor KNN object-oriented technology compared, S-SVM classifica- tion results is superior to the traditional object-oriented methods.
(2) Proposed object-oriented classification methods based on fuzzy support vector ma-chine theory, and the development of the corresponding sub-program (F-SVM). Proposed by using fuzzy support vector machine pattern classification method of object-oriented (F-SVM), firstly the object is given a weight using evaluation of fuzzy mathematics, and singular vector of the feature points in the weakening of the weight re-evaluate. The impact is under mini-mum level to the separating hyperplane. Experimental results show that the accuracy of the proposed method compared to standard support vector machine method improves the 5.1% or so to illustrate the proposed method improved the classification accuracy.
(3) New object-oriented classification methods is proposed based on least squares support vector theory, and the development of the two sub-programs S-LSSVM and FG-LSSVM.
First, direct selection feature objects, using a standard least squares support vector ma-chine model of the work of experimental data analysis results show that the use of S-LSSVM method, image recognition rate of 92.8%, compared to the same conditions of nearest neighbor KNN method is 95.2 %, SVM is 95.2 % increase by about 2.4 % or so. The results show that: A S-LSSVM object-oriented approach is not only a relatively faster computing speed, the detec-tion accuracy is also achieved under the same conditions in other object-oriented classification model.
Then, after this S-LSSVM subsystem, put forward a combinational approach, namely FG-LSSVM, hybridizing least squares support vector machines (LSSVM) with fuzzy and grey degree of correlation (FG), which presents a feasible high-precision image classification algo-rithm for land cover. To compare the performance with other object-oriented methods, with original samples, three models were successively verified, which were standard support vector machines (SVM) and the fuzzy nearness improved support vector machines (FSVM), and the traditional K nearest neighbor (KNN) object-oriented methods. A high precision land cover image classification system was established with the proposed approach. The results show the total precision of FG-LSSVM is about 2.4% higher than that of SVM and FSVM, and KNN object-oriented methods in the study area. Compared with S-LSSVM the FG-LSSVM increased by about 4.8 % or so. The proposed method also meets the requirements of land cover image classification in respect of e?ciency and e?ects.
3. Through introduction of computer vision recognition technology, level recognition idea is used in object-oriented system. By changing the scale, the large object layer is divided into separated the lowest level, and at this level of recognition model is to be built by using support vector machines to extract the large-scale land cover types. The rest of the small-scale features is established in a single-level, and in this layer another new support vector machine model is created to extract the type of small-scale surface features. Precise mask by computer visual technology, the extracted features in superposition of all levels get the final classification results.
Based on the above visual theory, this paper focuses on and proposes a new multi-level object-oriented method (Multi layer Object-oriented SVM, MOBIA-SVM). The classification work proposed theoretical framework, ideas, focusing on practical value and academic sig-nificance, for the algorithm application design and experimental. Classification results show that the object-oriented multi-level support vector classification method is not only suitable for large-scale remote sensing image, and classification accuracy improve significantly for small-scale object features.
The research was further deepening and improvement of support vector machine theory in the application of object - oriented remote sensing image classification and conducted useful explorations and providing technical support for land cover classification. At the same time, the promotion of computer vision technology, especially remote sensing image segmentation in the study of the classification has important practical significance.
在土地覆被分类应用领域,这种分割单元就是土地利用斑块。面向对象的分析方法主要特点是它识别和分类的目标是对象片段或区域,而不是单个像元,把这个识别分类过程建立在分割单元层次上的方法叫做面向对象遥感图像分类技术。面向对象分类技术优越点不仅在于充分考虑了地物的光谱特征,而且利用了地物之间的空间信息,因此,已经在一些应用领域得到有效的推广使用。但在面向对象土地覆被遥感图像分类技术中,基于核函数学习理论的分类方法尚待深入研究,特别是基于支持向量机理论的面向对象分类技术研究开发及应用方面缺少全面和深入的基础研究支持。
本文正是基于统计中的核函数学习理论,针对支持向量机在新型土地覆被面向对象遥感图像分类技术研究中亟待解决的理论框架问题与关键技术,依据统计学、计算图像工程学、遥感影像解译、数字图像处理学的相关原理,研究了基于支持向量机理论的面向对象分类技术,探索了支持向量机理论在面向对象问题上的应用潜力、能力和前景。具体开展了以下三个方面的研究工作(研究内容和创新点):
1.开始从广义遥感图像分类的最小单位即像素角度出发,总结了核密度函数在图像分类中已有的研究成果,分析了核密度函数在分类中的显著特点,提出了一个快速“核密度估计Bayes监督分类”土地覆被分类模型,并开发了一个分类子应用系统。
首先因为合理选择核函数是支持向量机分类方法的重要组成部分。其次,了解核函数的带宽参数特点,对于核密度梯度图像分割方法中重要尺度参数选择具有重要意义。核函数使支持向量机可以容易地是实现非线性映射运算。本章通过对经典高斯核函数的改进,初步认识核密度函数的带宽参数的特征及物理意义。研究结果表明:将该快速变换后的核函数应用于遥感图像土地覆被分类,达到了快速计算的目的,分类精度与同等条件下标准支持向量机相接近,实验结果也验证了改进后核函数跟踪样本分布的有效性。
本章的研究作为下一步面向对象分类的必要的基础。
2.以遥感图像核密度梯度图像分割理论为基础,建立在分割单元即对象层次上,逐步开展了基于支持向量机理论的面向对象图像分类技术研究工作和子应用系统开发工作。进行了一系列的面向对象层级基础性研究工作。以下研究方法和相应开发的子应用系统,归类于单一尺度面向对象分类技术( Single Layer Object-Oriented SVM,简记为SOBIA-SVM)体系中。
(1)提出了采用标准支持向量机理论的面向对象遥感图像分类方法,并开发了相应的子系统程序(S-SVM)。提出的建立在分割单元特征基础上的方法S-SVM,充分利用了支持向量机在小样本特征空间中的优良的识别特性。通过统计分析,说明提出的分类方法优于传统KNN方法。实验结果表明,该方法可以应用到遥感图像面向对象识别分类中。
(2)提出了基于模糊支持向量机理论的面向对象分类方法,并开发了相应子系统程序(F-SVM)。提出的采用模糊支持向量机的面向对象模式分类方法(F-SVM),对分割得到的对象单元进行了特征模糊数学权重评价,对特征中的“奇异”向量点进行弱化权重评价,使其对分类超平面的影响最低。通过统计分析,说明提出的分类方法优于未进行样本模糊权重处理的方法。实验结果表明,提出的方法精确度相比标准支持向量机方法提高了5.1%左右。
(3)提出了基于最小二乘支持向量理论的面向对象分类方法,并开发了两个子系统程序S-LSSVM和FG-LSSVM。
首先,对分割单元直接选择特征单元对象,利用标准最小二乘支持向量机进行模型的建立工作,实验数据表明,采用S-LSSVM方法,图像识别率达到了92.8%,对比相同测试样本条件下最近邻KNN方法为95.2%,SVM为95.2%,相差约2.4%左右,另外,直接S-LSSVM面向对象方法计算速度相对更快外,检测精度接近同等条件下S-SVM和KNN面向对象分类模型。
然后,在此S-LSSVM子系统基础上,深入研究并提出采用模糊灰色关联理论对样本单元进行预处理条件下的面向对象分类方法,并开发了相应的程序FG-LSSVM。提出采用最小二乘支持向量机与模糊灰色关联理论特征样本预先处理的新组合分类算法。实现了一个较高精度的遥感图像面向对象分类信息系统。实验结果表明,提出的FG-LSSVM面向对象分类方法相比较标准支持向量机S-SVM、模糊支持向量机F-SVM、以及最近邻KNN面向对象方法,其实验精度提高了2.4%左右,相比未进行特征样本预先处理的S-LSSVM算法,精度提高了约4.8%左右。提出的新方法在识别性能和精度上有了改善外,应用方面也和以上面向对象方法一样符合研究区实际分类应用的要求。
3.引入计算机视觉中的层次识别技术,通过改变遥感图像分割尺度,把大分割单元对象层划分到最底层,并在该层次建立起支持向量机识别模型,从而先行提取出大尺度层次上土地覆被地物来,剩下的小尺度地物单独建立一个层次,并在该层以小尺度参数重新分割图像,并且建立新的支持向量机模型,提取小尺度地物类型。通过计算机视觉中的图像准确掩模编程技术,将各层级提取的地物掩模叠加,得到最后的分类结果。
本章是建立在第二部分的单一尺度面向对象分类研究(SOBIA-SVM)体系基础之上,并重点研究提出一个新的多层次的面向对象方法( Multi-Layer Object-OrientedSVM,简记为MOBIA-SVM),提出了分类理论框架,思路侧重以实用价值和学术意义为出发点,进行了算法设计和应用实验,研究结果表明,提出的多层次支持向量面向对象分类方法,不仅适用大幅面尺度遥感图像分类,尤其是对小尺度地物分类精确度提高显著。此方法作为加快实现实际应用研究提供了基础研究支撑。
结束语本项研究为进一步深化和完善支持向量机理论在面向对象遥感图像分类中的应用,进行了有益的探索并提供了技术支撑,也为土地覆被面向对象分类技术的发展,提供了基础研究参考。同时对计算机视觉技术应用,特别是遥感数字图像分割技术进一步研究,具有重要的实际促进意义。
Object-oriented remote sensing image analysis is one of the technology development of remote sensing images. Object-oriented remote sensing image analysis method, also known as cell-based segmentation method of image analysis, while the object is picked up through a certain image segmentation algorithm for remote sensing image, to get relatively homogeneity within the object, or even higher uniformity degree of image fragments or regional units.
In the application of land cover classification domain, this unit is the land use patch.Object- oriented analysis method of main characteristic is its recognition and classification target is object fragments or regions, rather than a single pixel. We make this identification and classification of process which based on segmentation method of unit level is called the object - oriented remote sensing image classification techniques.
The superiority of object - oriented classification technology is not only to give full con-sideration to the spectral characteristics of the surface features, and take advantage of the spatial information between surface features. Therefore, this technology has been e?ective promotion in many application fields. However, in object-oriented land cover remote sensing image classi-fication techniques, this methodology based on kernel function method of learning theory need to be in-depth study. In particular, the introduction of support vector machine technology, the application of the technique is lack of comprehensive and in-depth basic research support.
The research is focused on as well-known urgently need to be solved for the theoretical problems and key technologies in the new land cover object-oriented technology, which based on kernel learning theory, support vector machine applied research in remote sensing image, according to the related principles of digital image processing, statistics, computing image en-gineering, remote sensing, image interpretation, researching on object - oriented classification based on support vector machine theory, exploring using support vector machine theory on the issue of object - oriented application potential, ability and prospects. Specific research content and its related innovation is carried out as the following three aspects(research contents and some innovations ):
1. Starting from the generalized minimum unit which is as pixels of remote sensing image classification perspective, summed up kernel density function in image classification research results, analyzed some notable feature of kernel density function classification, put forward a rapid“The kernel density estimation of Bayes classification”of land cover classification model and established a classification system. As take the preliminary work of object-oriented analysis for the further research.
First of all, a reasonable choice for the kernel function is an important part to support vector machine approach, and this step is also an important for scale parameter selection in the nuclear density gradient image segmentation method. Kernel function can easily make support vector machine to achieve non-linear algorithms. By classical Gaussian kernel function improvement, an initial understanding is acquired on the bandwidth parameters of the nuclear density function, as well as its characteristics and physical meaning. The results show that: The rapid transformation of nuclear function is applied to remote sensing images of land cover classification to achieve a quick calculation purposes, Classification accuracy under the same conditions with the standard support vector machines are compared, The experimental results also verified the improved kernel function to track the e?ectiveness of the sample.
This section studies as basic research pave the way as next step by using Support Vec-tor Machine technology and the kernel density gradient image segmentation theory of object-oriented classification
2. Based on the theory of kernel density gradient of remote sensing image segmentation,on object level, we step by step carried out classification theory and technology research work and system establishment work based on Support Vector Machines. We carried out a series of object-level basic research. This work is classified in a single-scale object-oriented classifica- tion (SOBIA-SVM).
(1) We provided an object-oriented remote sensing image classification method based on the standard support vector machine theory, and the development of the corresponding sub-program (S-SVM). Proposed methods of cell characteristics of S-SVM which is based on seg-mentation, its full use of excellent identification feature of support vector machine in small sample feature space. Experimental data analysis results show that the method can be applied to the object-oriented high-resolution remote sensing image recognition category. And with the traditional the nearest neighbor KNN object-oriented technology compared, S-SVM classifica- tion results is superior to the traditional object-oriented methods.
(2) Proposed object-oriented classification methods based on fuzzy support vector ma-chine theory, and the development of the corresponding sub-program (F-SVM). Proposed by using fuzzy support vector machine pattern classification method of object-oriented (F-SVM), firstly the object is given a weight using evaluation of fuzzy mathematics, and singular vector of the feature points in the weakening of the weight re-evaluate. The impact is under mini-mum level to the separating hyperplane. Experimental results show that the accuracy of the proposed method compared to standard support vector machine method improves the 5.1% or so to illustrate the proposed method improved the classification accuracy.
(3) New object-oriented classification methods is proposed based on least squares support vector theory, and the development of the two sub-programs S-LSSVM and FG-LSSVM.
First, direct selection feature objects, using a standard least squares support vector ma-chine model of the work of experimental data analysis results show that the use of S-LSSVM method, image recognition rate of 92.8%, compared to the same conditions of nearest neighbor KNN method is 95.2 %, SVM is 95.2 % increase by about 2.4 % or so. The results show that: A S-LSSVM object-oriented approach is not only a relatively faster computing speed, the detec-tion accuracy is also achieved under the same conditions in other object-oriented classification model.
Then, after this S-LSSVM subsystem, put forward a combinational approach, namely FG-LSSVM, hybridizing least squares support vector machines (LSSVM) with fuzzy and grey degree of correlation (FG), which presents a feasible high-precision image classification algo-rithm for land cover. To compare the performance with other object-oriented methods, with original samples, three models were successively verified, which were standard support vector machines (SVM) and the fuzzy nearness improved support vector machines (FSVM), and the traditional K nearest neighbor (KNN) object-oriented methods. A high precision land cover image classification system was established with the proposed approach. The results show the total precision of FG-LSSVM is about 2.4% higher than that of SVM and FSVM, and KNN object-oriented methods in the study area. Compared with S-LSSVM the FG-LSSVM increased by about 4.8 % or so. The proposed method also meets the requirements of land cover image classification in respect of e?ciency and e?ects.
3. Through introduction of computer vision recognition technology, level recognition idea is used in object-oriented system. By changing the scale, the large object layer is divided into separated the lowest level, and at this level of recognition model is to be built by using support vector machines to extract the large-scale land cover types. The rest of the small-scale features is established in a single-level, and in this layer another new support vector machine model is created to extract the type of small-scale surface features. Precise mask by computer visual technology, the extracted features in superposition of all levels get the final classification results.
Based on the above visual theory, this paper focuses on and proposes a new multi-level object-oriented method (Multi layer Object-oriented SVM, MOBIA-SVM). The classification work proposed theoretical framework, ideas, focusing on practical value and academic sig-nificance, for the algorithm application design and experimental. Classification results show that the object-oriented multi-level support vector classification method is not only suitable for large-scale remote sensing image, and classification accuracy improve significantly for small-scale object features.
The research was further deepening and improvement of support vector machine theory in the application of object - oriented remote sensing image classification and conducted useful explorations and providing technical support for land cover classification. At the same time, the promotion of computer vision technology, especially remote sensing image segmentation in the study of the classification has important practical significance.
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