基于纹理特征的遥感影像居民地提取技术研究
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摘要
随着传感器技术、航空和航天平台技术、数据通信技术的发展，现代遥感技术已经进入一个能够动态、快速、准确、多手段获取多种对地观测数据的新阶段。从遥感影像中提取的空间信息已成为地理信息获取的重要途径，并广泛应用于国民经济建设以及军事测绘保障。本文重点研究了基于纹理的遥感影像居民地提取算法，其核心是居民地纹理方向的检测以及在此基础上的特征提取等相关技术。
本文的主要内容和创新点如下：
1.介绍了遥感影像理解的概念、意义和难点，总结了地物提取的基本方法、研究现状和发展方向，分析了居民地提取的重点和难点，明确了本文的研究范围和基本思路。
2.剖析了居民地、植被、水域等典型地物在遥感影像上的表现形式，定性的描述了其影像的纹理特征，给出了纹理新的定义；总结了不同纹理测度表达和距离量测的方法，设计了基于纹理特征的提取流程。
3.详细分析了频谱分析、Gabor变换、共生矩阵、多尺度自卷积、Tamura纹理的原理和方法，依据居民地纹理的主方向，设计了特征构建方案和提取策略。
4.提出了基于频谱分析的居民地提取方法、基于纹理方向Gabor变换的居民地提取方法。频谱分析方法以低频信息、主方向高频信息在空间影像中的贡献赋以不同的权值，构建区分度好、相关程度低的特征向量；纹理方向Gabor变换方法以纹理的2个主方向和相应的中心频率设计滤波器，将特征向量由40维降低至2维，解决了滤波器组计算量过大的瓶颈问题，同时增强了特征的聚焦分析能力。
5.提出了基于居民地纹理方向灰度共生矩阵的构造方法、改进了多尺度共生矩阵的构造方法。纹理方向灰度共生矩阵方法依据纹理的2个主方向确定共生矩阵的方向，同时，依据像元对的方向，将滑动窗口由常规的正方形更改为矩形，改善了特征描述的针对性和有效性；多尺度共生矩阵方法将纹理第一主方向旋转至水平方向并进行非下采样小波变换，在不同尺度的低频、0o方向高频和90o方向高频分别构造相应的共生矩阵。结合特征的物理意义和相关性分析，保留冗余度小、相关程度低的测度用于居民地的提取。
6.提出了基于Tamura对比度和MSA直方图的居民地提取方法。比较分析了MSA直方图特征并将其应用于遥感影像居民地提取，该特征具有仿射不变特性，但对噪声敏感，针对该问题，引入Tamura对比度测度，提升了算法的抗噪性和精准度。
7.针对算法的改进和创新进行了相关实验和精度评估，同时与未改进算法的提取结果进行了比较，验证了算法改进和创新的有效性。
With the development of sensor technology, airborne and spaceborne platform technologyand data communication technology, remote sensing technology has entered a new stage. And itis capable of accessing various types of earth observation data dynamically, rapidly, accuratelyand by multiple means. Extracting spatial information from remote sensing imagery has becomean important approach for geographic information acquisition. And the exacted information iswidely used in the national economy development and military surveying and mapping support.In the thesis, the focus is put on the studying of image texture based residents extractionalgorithm, the core of which is resident direction detection, the subsequent feature extraction andother related techniques.
The main contents and innovations in the thesis are listed as follows:
1. Introduction was given on the concept, significance and difficulties of remote sensingimage understanding, and summarization was made on the basic methods, research status anddeveloping trend of ground object extraction. Analysis was carried out on the emphases anddifficulties of residents extraction. And the research scope and basic ideas in the thesis wasdefined.
2. Analysis was made on the expression forms of the residents, vegetation, water and theother typical features in remote sensing images, and qualitative description was given on theirtexture characteristics in the image. The expression methods, which are based on differenttexture indexes, and distance measurement algorithms were summarized, and an extractionprocess based on texture feature was then designed.
3. A detailed analysis was given on the principle and method of spectrum analysis, Gabortransform, co-occurrence matrix, multi-scale autoconvolution and Tamura texture. Based on themain direction of residential texture, feature construction strategies and extraction schemes weredesigned.
4. One residents extraction method based on spectrum analysis and one Gabor transformmethod based on residents texture direction were brought forward. Based upon the contributionof the above three features in the image space, different weights were assigned to the features toconstitute a feature vector in the first method. The new vector is characteristic of low correlationand strong differentiation among different features. The feature vector was reduced from40dimensions to2dimensions, which solved the bottleneck existed in huge filter groupcomputation, and at the same time, enhanced the ability of feature focusing analysis.in he secondmethod
5. One gray co-occurrence matrix construction method based on residents texture directionwas put forward and the construction method of multi-scale co-occurrence matrix was improved.The co-occurrence matrix direction was determined by two main texture directions, which is thestep ratio of pixel couples in the vertical and horizontal direction. Meanwhile, according to thedirection of pixel couples, the sliding window is changed from the conventional square torectangular and pertinence and validity of feature description is greatly improved. In the secondmethod, the first main texture direction was rotated to horizontal direction and then sampled bywavelet transform. On various scale low frequency,0oand90odirection high frequency,corresponding co-occurrence matrix was constructed. Combining the physical meaning andcorrelation analysis of features, measures of low redundancy and low correlation were selectedand then used for residents extraction.
6. Based on Tamura contrast and MSA histogram, a residents extraction method wasproposed. MSA histogram features were compared, analyzed and then used for residentsextraction form remote sensing images. Since MSA histogram features were sensitive to noise,the measure of Tamura contrast was introduced to improve the anti-noise performance andprecision of the algorithm.
7. According to the improvements and innovations of our algorithms, experiments weredesigned and analysis was made. Comparing with the results obtained by the original and theother algorithms, the effectiveness of our algorithms was verified.
本文的主要内容和创新点如下：
1.介绍了遥感影像理解的概念、意义和难点，总结了地物提取的基本方法、研究现状和发展方向，分析了居民地提取的重点和难点，明确了本文的研究范围和基本思路。
2.剖析了居民地、植被、水域等典型地物在遥感影像上的表现形式，定性的描述了其影像的纹理特征，给出了纹理新的定义；总结了不同纹理测度表达和距离量测的方法，设计了基于纹理特征的提取流程。
3.详细分析了频谱分析、Gabor变换、共生矩阵、多尺度自卷积、Tamura纹理的原理和方法，依据居民地纹理的主方向，设计了特征构建方案和提取策略。
4.提出了基于频谱分析的居民地提取方法、基于纹理方向Gabor变换的居民地提取方法。频谱分析方法以低频信息、主方向高频信息在空间影像中的贡献赋以不同的权值，构建区分度好、相关程度低的特征向量；纹理方向Gabor变换方法以纹理的2个主方向和相应的中心频率设计滤波器，将特征向量由40维降低至2维，解决了滤波器组计算量过大的瓶颈问题，同时增强了特征的聚焦分析能力。
5.提出了基于居民地纹理方向灰度共生矩阵的构造方法、改进了多尺度共生矩阵的构造方法。纹理方向灰度共生矩阵方法依据纹理的2个主方向确定共生矩阵的方向，同时，依据像元对的方向，将滑动窗口由常规的正方形更改为矩形，改善了特征描述的针对性和有效性；多尺度共生矩阵方法将纹理第一主方向旋转至水平方向并进行非下采样小波变换，在不同尺度的低频、0o方向高频和90o方向高频分别构造相应的共生矩阵。结合特征的物理意义和相关性分析，保留冗余度小、相关程度低的测度用于居民地的提取。
6.提出了基于Tamura对比度和MSA直方图的居民地提取方法。比较分析了MSA直方图特征并将其应用于遥感影像居民地提取，该特征具有仿射不变特性，但对噪声敏感，针对该问题，引入Tamura对比度测度，提升了算法的抗噪性和精准度。
7.针对算法的改进和创新进行了相关实验和精度评估，同时与未改进算法的提取结果进行了比较，验证了算法改进和创新的有效性。
With the development of sensor technology, airborne and spaceborne platform technologyand data communication technology, remote sensing technology has entered a new stage. And itis capable of accessing various types of earth observation data dynamically, rapidly, accuratelyand by multiple means. Extracting spatial information from remote sensing imagery has becomean important approach for geographic information acquisition. And the exacted information iswidely used in the national economy development and military surveying and mapping support.In the thesis, the focus is put on the studying of image texture based residents extractionalgorithm, the core of which is resident direction detection, the subsequent feature extraction andother related techniques.
The main contents and innovations in the thesis are listed as follows:
1. Introduction was given on the concept, significance and difficulties of remote sensingimage understanding, and summarization was made on the basic methods, research status anddeveloping trend of ground object extraction. Analysis was carried out on the emphases anddifficulties of residents extraction. And the research scope and basic ideas in the thesis wasdefined.
2. Analysis was made on the expression forms of the residents, vegetation, water and theother typical features in remote sensing images, and qualitative description was given on theirtexture characteristics in the image. The expression methods, which are based on differenttexture indexes, and distance measurement algorithms were summarized, and an extractionprocess based on texture feature was then designed.
3. A detailed analysis was given on the principle and method of spectrum analysis, Gabortransform, co-occurrence matrix, multi-scale autoconvolution and Tamura texture. Based on themain direction of residential texture, feature construction strategies and extraction schemes weredesigned.
4. One residents extraction method based on spectrum analysis and one Gabor transformmethod based on residents texture direction were brought forward. Based upon the contributionof the above three features in the image space, different weights were assigned to the features toconstitute a feature vector in the first method. The new vector is characteristic of low correlationand strong differentiation among different features. The feature vector was reduced from40dimensions to2dimensions, which solved the bottleneck existed in huge filter groupcomputation, and at the same time, enhanced the ability of feature focusing analysis.in he secondmethod
5. One gray co-occurrence matrix construction method based on residents texture directionwas put forward and the construction method of multi-scale co-occurrence matrix was improved.The co-occurrence matrix direction was determined by two main texture directions, which is thestep ratio of pixel couples in the vertical and horizontal direction. Meanwhile, according to thedirection of pixel couples, the sliding window is changed from the conventional square torectangular and pertinence and validity of feature description is greatly improved. In the secondmethod, the first main texture direction was rotated to horizontal direction and then sampled bywavelet transform. On various scale low frequency,0oand90odirection high frequency,corresponding co-occurrence matrix was constructed. Combining the physical meaning andcorrelation analysis of features, measures of low redundancy and low correlation were selectedand then used for residents extraction.
6. Based on Tamura contrast and MSA histogram, a residents extraction method wasproposed. MSA histogram features were compared, analyzed and then used for residentsextraction form remote sensing images. Since MSA histogram features were sensitive to noise,the measure of Tamura contrast was introduced to improve the anti-noise performance andprecision of the algorithm.
7. According to the improvements and innovations of our algorithms, experiments weredesigned and analysis was made. Comparing with the results obtained by the original and theother algorithms, the effectiveness of our algorithms was verified.
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