高分辨率遥感影像多尺度纹理、形状特征提取与面向对象分类研究
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摘要
高分辨率遥感影像(如QuickBird, IKONOS,SPOT-5等),能够提供大量的地面细节信息,因此展开了遥感应用的新纪元。但是,这种新型数据却对影像特征提取和分类提出了新的问题和挑战。由于影像复杂的空间排列,以及同类地物内部的光谱异质性,使得传统的光谱解译方法对高分辨率影像无法取得好的效果。高分辨率影像中,可以观测到更多的细节特征和小目标,这造成了同种地物内部的光谱变化和异质性,也使得不同地物之间的光谱差异减少。这种较高的类内变化和较低的类间差异,降低了各种地面覆盖物在光谱域的模式可分性。为了克服这一问题,需要有效的利用影像的纹理、结构、尺度和对象信息,弥补光谱特征的不足。
本文分别从纹理、形状和对象的角度来提取和分析影像的特征,在此基础上,研究这些空间信息的多尺度特性,最后,用三个高分辨率影像应用的实例(城市、农业和森林区域)来验证本文所提出的各种方法。
在论文展开之前,我们首先分析了分类器对高分辨率影像解译的影响。本文提出使用机器学习和人工智能方法来处理高分辨率数据,并在实验中用多层感知器(MLP)、概率神经网络(PNN)、支持向量机(SVM)和关系向量机(RVM)进行了测试,结果发现：由于机器学习算法的非参数化性质,和自适应学习的能力,使之能够获得比传统的统计分类器(如极大似然法MLC、距离分类器MDM、光谱角制图SAM)更好的结果。
但同时,实验结果也表明：尽管先进的分类方式能够提供更高的精度,但它们的结果仍然显示出某些系统性的误差。这种错误是无法通过分类器的革新来消除的,必须加入更多的决策信息,如更多的波段,纹理和结构信息等。换句话说,解决光谱相似性目标的区分需要更多的信息。因此,本文接下来将重点研究高分辨率影像的纹理、形状和面向对象特征。
对于纹理信息提取,本文提出一种基于NSCT变换(非下采样小轮廓变换)的多方向多尺度纹理特征。NSCT变换可以分为两个具有移动不变性的部分：1)满足多尺度特性的非下采样金字塔结构,2)满足方向性的方向滤波簇。NSCT变换是小波变换的改进形式,它的优势主要在于对影像边缘和线特征的表达,因此,NSCT对于高分辨率影像的纹理结构特征提取更有潜力。实验结果显示：纹理信息的加入在视觉上和精度上都要优于传统的光谱分类方式。而且,NSCT特征得到了比小波纹理、GLCM纹理和空间自相关测度更好的结果。
形状和结构特征是高分辨率影像的一个显著特性,它对人类的视觉更为敏感。我们原创性的提出一种像元形状指数(PSI),用来描述中心像元局部邻域的形状和轮廓信息。实验比较了PSI与小波纹理以及GLCM纹理特征,结果显示：PSI能够有效的描述上下文形状信息,提供了更高的精度。
同时,在方向线直方图基础上,不仅可以提取PSI指数,也可以提取更多的结构指数(PSI的扩展算法),如：长、宽、长宽比、直方图方差等等,以弥补PSI特征的单一性。然后,针对多波段结构特征的相似性,本文提出一种特征相似性指数的维数减少算法,减少信息冗余和计算代价,并用BP神经网络、概率神经网络PNN,以及支持向量机来解译多元结构特征。QuickBird数据集和HYDICE航空影像数据的实验表明：SFS特征集能够得到比PSI更好的效果。
对于对象信息的提取和影像的面向对象分析,本文针对eCognition软件的不足,提出一种自适应均值移动框架。其基本步骤是用均值移动来获取高分辨率影像的面向对象表达形式,然后用SVM进行解译。为了有效的利用均值移动分析,我们提出两种自适应带宽尺度算法：一种是基于影像局部结构(非监督),另一种是基于类别可分性测度(监督)。实验影像采用HYDICE航空数据和HYMAP航空影像。我们把提出的自适应均值移动算法和面向对象分析软件eCognition进行了详细的比较,结果说明了均值移动方法的稳健型和效率。
在多元特征提取(纹理、结构和对象)的基础上,本文进一步研究了这些特征的多尺度性质,把纹理、结构和对象特征在尺度维进行扩展,研究多尺度特征表达和多尺度信息融合方法。本文提出了三种多尺度融合方案：矢量叠力口(VS),SVM模糊输出(Fuzzy SVM),以及多分类器投票(Voting)。VS方法是把多尺度信息在特征空间进行叠加。模糊SVM方法的基本原理是对每个尺度的SVM输出函数值进行模糊化,选择不确定性最小的作为最优决策尺度和最优决策结果。实验用多个高分辨率数据集对四种多尺度特征表达方法进行了详细的测试,结果显示特征叠加和模糊SVM都能够有效的融合多尺度信息,达到单尺度下的最优精度。同时,实验说明：VS多尺度叠加的方法能够利用特征在尺度维的扩展,形成高维的尺度特征空间,在大多数测试结果中,VS能提供优于模糊SVM和Voting的结果。
最后用三个高分辨率遥感应用问题来验证本文提出的多尺度和多特征方法。具体包括：1)对LiDAR影像进行纹理特征、统计特征提取,并对航空影像和相应的LiDAR数据进行信息融合,对Denmark,Odense区域进行高精度高分辨率城市地面覆盖制图；2)使用多尺度形态学特征、多层面向对象分析方法,用IKONOS多光谱影像对Panama的Caribbean海岸的红树林进行树种区分和森林物种空间分布研究；3)使用多尺度光谱—纹理方式,用PHI航空影像,对江苏省常州市夏桥农业区域进行农作物物种识别和精细农业制图。
Very high resolution (VHR) remotely sensed imagery, such as QuickBird, IKONOS, SPOT-5, can provide a large amount of information, thus opening up avenues for new remote sensing applications. However, their availability poses challenges to image classification. Due to the complex spatial arrangement and spectral heterogeneity even within the same class, conventional spectral classification methods are grossly inadequate for classification of VHR imagery. Detailed features and small objects can be detected in VHR images and, consequently, the spectral signatures inside an information class become more heterogeneous and different objects become more spectrally similar. The resulting high intraclass and low interclass variabilities lead to a reduction in the statistical separability of the different land-cover classes in the spectral domain. In order to overcome the inadequacy, the textural, strauctural, scale and object-based features should be exploited effectively, in order to complement the spectral feature space.
This paper aims to investigate the textural, shape and object-based features from VHR imagery. Furthermore, these spatial features are extended to multiscale approaches. Afterwards, three case studies on urban, agricultural and forest regions were conducted for validation and application for the proposed algorithms in this paper.
At first, we analyzed the effects and performance of different classifiers for VHR image interpretation. Accordingly, we proposed to use the neural network classifiers and machine learning approaches. The notable advantages of these classification approaches consist in the adaptive learning rule and the non-parametric characteristic, which is especially efficient for the complex data distribution of VHR imagery. In experiments, the MLP (Multi-Level Perceptron), PNN (Probability Neural Network), SVM (Support Vector Machines), and RVM (Relevance Vector Machine) were employed. The experiments on the HYDICE airborne dataset revealed that the machine learning approaches substantially outperformed the conventional classifiers, such as MLC (Maximum Likelihood Classification), MDM (Minimum Distance to Mean), SAM (Spectral Angular Mapping) etc.
However, the experimental results revealed that although the advanced classification techniques could give higher accuracies, their results also showed some confident mis-allocations. These mis-classifications should be improved using further discriminatory variables (e.g. additional wavelengths, textural and structural information). In other words, some additional information is essential for the recognition of spectrally similar classes. Therefore, in this paper, we focused on the texture, shape and object-based feature extraction from the VHR imagery.
For the texture information extraction, in this paper, a novel multiscale and multidirectional texture measure based on the NSCT transform was proposed. NSCT transform can be divided into two shift-invariant parts:1) a non-subsampled pyramid structure that satisfies the multiscale property and 2) a directional filter bank that ensures directionality. The advantages of NSCT over the wavelet consist in the representations of image edges and lines, therefore, it is more potential for textural and structural feature extraction from the VHR imagery. The experiments showed the introduction of additional texture information could obviously improve the spectral classification in terms of both visual inspection and statistical accuracies. In the other hand, the proposed NSCT feature gave higher accuracy than the GLCM textures, spatial autocorrelation measure and the feature based on stationary wavelet transform.
The shape and structural features are of interest because they are more sensitive to the human vision system and they can provide more discriminative information. We proposed a novel shape feature index, namely pixel shape index (PSI), to describe the shape and contour in a local area surrounding a central pixel. PSI is a pixel based feature, which measures the gray similarity distance in multiple directions. The results of PSI were compared with some spatial features extracted using wavelet transform (WT), gray level co-occurrence matrix (GLCM) in order to test its effectiveness. The experiments demonstrated that PSI was capable of describing the shape features effectively and resulted in more accurate classifications than other methods.
Based on the extracted direction-lines histogram, we proposed an extension of PSI algorithm, namely SFS (structural feature set). Some new statistical measures were designed to extract structural features from the direction-lines, such as weighted mean, length-width ratio, and standard deviation, in order to overcome the inadequacy of the PSI. Afterwards, some dimension reduction approaches were employed in order to reduce information redundancy. BPNN, EM-PNN and SVM were used to process the hybrid spectral-structural features after the steps of spatial feature extraction and dimension reduction. The proposed SFS approach was evaluated using two QuickBird datasets and the HYDICE Washington dataset. The results revealed that the new set of reduced spatial features had better performance than PSI.
With respect to the object-based analysis, an adaptive mean shift analysis framework was proposed for object extraction and classification of VHR imagery. The basic idea is to apply a mean shift to obtain an object-oriented representation of VHR data and then use support vector machine to interpret the feature set. In order to employ mean shift effectively, two bandwidth selection algorithms were proposed for the mean shift procedure. One is based on the local structure and the other exploits separability analysis. Experiments were conducted on two VHR datasets, the DC Mall HYDICE image and the Purdue campus HYMAP image. We evaluated and compared the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely the derivative of the morphological profile (DMP). Experimental results verified that the proposed mean shift-based analysis system was robust and obviously outperformed other methods.
The proposed textural, shape and object-based features are extended to multiscale approaches. We aim to 1) extract multiscale features from VHR urban imagery and 2) integrate the multiscale information using three approaches: vector-stacking (VS) SVM, Fuzzy SVM and multi-classifier voting. The VS SVM concatenates the multilevel features in a single SVM. The fuzzy approach deals with the SVM function values for each scale and then chooses the optimal scale according to the membership value. In experiments, three VHR datasets were used for validation of the presented multiscale fusion schemes:ROSIS Pavia datasets and the HYDICE DC Mall image. Experiments showed that both vector-stacking and fuzzy approaches were able to give comparable or higher results than that one obtained by the optimal scale in terms of accuracies. In most cases, the VS SVM provided higher accuracies than the Fuzzy SVM and multi-classifier voting.
At last, we used three case studies to validate and test the multiscale features and the classification methods proposed in this paper. The applications involved 1) the feature analysis and information fusion using aerial photograph and LiDAR data, 2) the mangrove mapping from IKONOS multispectral imagery using multiscale texture information in a study area on the Caribbean coast of Panama, and 3) detailed agriculture mapping using multiscale spectral-textural method based on the PHI airborne data in Xiaqiao, Jiangsu Province.
本文分别从纹理、形状和对象的角度来提取和分析影像的特征,在此基础上,研究这些空间信息的多尺度特性,最后,用三个高分辨率影像应用的实例(城市、农业和森林区域)来验证本文所提出的各种方法。
在论文展开之前,我们首先分析了分类器对高分辨率影像解译的影响。本文提出使用机器学习和人工智能方法来处理高分辨率数据,并在实验中用多层感知器(MLP)、概率神经网络(PNN)、支持向量机(SVM)和关系向量机(RVM)进行了测试,结果发现：由于机器学习算法的非参数化性质,和自适应学习的能力,使之能够获得比传统的统计分类器(如极大似然法MLC、距离分类器MDM、光谱角制图SAM)更好的结果。
但同时,实验结果也表明：尽管先进的分类方式能够提供更高的精度,但它们的结果仍然显示出某些系统性的误差。这种错误是无法通过分类器的革新来消除的,必须加入更多的决策信息,如更多的波段,纹理和结构信息等。换句话说,解决光谱相似性目标的区分需要更多的信息。因此,本文接下来将重点研究高分辨率影像的纹理、形状和面向对象特征。
对于纹理信息提取,本文提出一种基于NSCT变换(非下采样小轮廓变换)的多方向多尺度纹理特征。NSCT变换可以分为两个具有移动不变性的部分：1)满足多尺度特性的非下采样金字塔结构,2)满足方向性的方向滤波簇。NSCT变换是小波变换的改进形式,它的优势主要在于对影像边缘和线特征的表达,因此,NSCT对于高分辨率影像的纹理结构特征提取更有潜力。实验结果显示：纹理信息的加入在视觉上和精度上都要优于传统的光谱分类方式。而且,NSCT特征得到了比小波纹理、GLCM纹理和空间自相关测度更好的结果。
形状和结构特征是高分辨率影像的一个显著特性,它对人类的视觉更为敏感。我们原创性的提出一种像元形状指数(PSI),用来描述中心像元局部邻域的形状和轮廓信息。实验比较了PSI与小波纹理以及GLCM纹理特征,结果显示：PSI能够有效的描述上下文形状信息,提供了更高的精度。
同时,在方向线直方图基础上,不仅可以提取PSI指数,也可以提取更多的结构指数(PSI的扩展算法),如：长、宽、长宽比、直方图方差等等,以弥补PSI特征的单一性。然后,针对多波段结构特征的相似性,本文提出一种特征相似性指数的维数减少算法,减少信息冗余和计算代价,并用BP神经网络、概率神经网络PNN,以及支持向量机来解译多元结构特征。QuickBird数据集和HYDICE航空影像数据的实验表明：SFS特征集能够得到比PSI更好的效果。
对于对象信息的提取和影像的面向对象分析,本文针对eCognition软件的不足,提出一种自适应均值移动框架。其基本步骤是用均值移动来获取高分辨率影像的面向对象表达形式,然后用SVM进行解译。为了有效的利用均值移动分析,我们提出两种自适应带宽尺度算法：一种是基于影像局部结构(非监督),另一种是基于类别可分性测度(监督)。实验影像采用HYDICE航空数据和HYMAP航空影像。我们把提出的自适应均值移动算法和面向对象分析软件eCognition进行了详细的比较,结果说明了均值移动方法的稳健型和效率。
在多元特征提取(纹理、结构和对象)的基础上,本文进一步研究了这些特征的多尺度性质,把纹理、结构和对象特征在尺度维进行扩展,研究多尺度特征表达和多尺度信息融合方法。本文提出了三种多尺度融合方案：矢量叠力口(VS),SVM模糊输出(Fuzzy SVM),以及多分类器投票(Voting)。VS方法是把多尺度信息在特征空间进行叠加。模糊SVM方法的基本原理是对每个尺度的SVM输出函数值进行模糊化,选择不确定性最小的作为最优决策尺度和最优决策结果。实验用多个高分辨率数据集对四种多尺度特征表达方法进行了详细的测试,结果显示特征叠加和模糊SVM都能够有效的融合多尺度信息,达到单尺度下的最优精度。同时,实验说明：VS多尺度叠加的方法能够利用特征在尺度维的扩展,形成高维的尺度特征空间,在大多数测试结果中,VS能提供优于模糊SVM和Voting的结果。
最后用三个高分辨率遥感应用问题来验证本文提出的多尺度和多特征方法。具体包括：1)对LiDAR影像进行纹理特征、统计特征提取,并对航空影像和相应的LiDAR数据进行信息融合,对Denmark,Odense区域进行高精度高分辨率城市地面覆盖制图；2)使用多尺度形态学特征、多层面向对象分析方法,用IKONOS多光谱影像对Panama的Caribbean海岸的红树林进行树种区分和森林物种空间分布研究；3)使用多尺度光谱—纹理方式,用PHI航空影像,对江苏省常州市夏桥农业区域进行农作物物种识别和精细农业制图。
Very high resolution (VHR) remotely sensed imagery, such as QuickBird, IKONOS, SPOT-5, can provide a large amount of information, thus opening up avenues for new remote sensing applications. However, their availability poses challenges to image classification. Due to the complex spatial arrangement and spectral heterogeneity even within the same class, conventional spectral classification methods are grossly inadequate for classification of VHR imagery. Detailed features and small objects can be detected in VHR images and, consequently, the spectral signatures inside an information class become more heterogeneous and different objects become more spectrally similar. The resulting high intraclass and low interclass variabilities lead to a reduction in the statistical separability of the different land-cover classes in the spectral domain. In order to overcome the inadequacy, the textural, strauctural, scale and object-based features should be exploited effectively, in order to complement the spectral feature space.
This paper aims to investigate the textural, shape and object-based features from VHR imagery. Furthermore, these spatial features are extended to multiscale approaches. Afterwards, three case studies on urban, agricultural and forest regions were conducted for validation and application for the proposed algorithms in this paper.
At first, we analyzed the effects and performance of different classifiers for VHR image interpretation. Accordingly, we proposed to use the neural network classifiers and machine learning approaches. The notable advantages of these classification approaches consist in the adaptive learning rule and the non-parametric characteristic, which is especially efficient for the complex data distribution of VHR imagery. In experiments, the MLP (Multi-Level Perceptron), PNN (Probability Neural Network), SVM (Support Vector Machines), and RVM (Relevance Vector Machine) were employed. The experiments on the HYDICE airborne dataset revealed that the machine learning approaches substantially outperformed the conventional classifiers, such as MLC (Maximum Likelihood Classification), MDM (Minimum Distance to Mean), SAM (Spectral Angular Mapping) etc.
However, the experimental results revealed that although the advanced classification techniques could give higher accuracies, their results also showed some confident mis-allocations. These mis-classifications should be improved using further discriminatory variables (e.g. additional wavelengths, textural and structural information). In other words, some additional information is essential for the recognition of spectrally similar classes. Therefore, in this paper, we focused on the texture, shape and object-based feature extraction from the VHR imagery.
For the texture information extraction, in this paper, a novel multiscale and multidirectional texture measure based on the NSCT transform was proposed. NSCT transform can be divided into two shift-invariant parts:1) a non-subsampled pyramid structure that satisfies the multiscale property and 2) a directional filter bank that ensures directionality. The advantages of NSCT over the wavelet consist in the representations of image edges and lines, therefore, it is more potential for textural and structural feature extraction from the VHR imagery. The experiments showed the introduction of additional texture information could obviously improve the spectral classification in terms of both visual inspection and statistical accuracies. In the other hand, the proposed NSCT feature gave higher accuracy than the GLCM textures, spatial autocorrelation measure and the feature based on stationary wavelet transform.
The shape and structural features are of interest because they are more sensitive to the human vision system and they can provide more discriminative information. We proposed a novel shape feature index, namely pixel shape index (PSI), to describe the shape and contour in a local area surrounding a central pixel. PSI is a pixel based feature, which measures the gray similarity distance in multiple directions. The results of PSI were compared with some spatial features extracted using wavelet transform (WT), gray level co-occurrence matrix (GLCM) in order to test its effectiveness. The experiments demonstrated that PSI was capable of describing the shape features effectively and resulted in more accurate classifications than other methods.
Based on the extracted direction-lines histogram, we proposed an extension of PSI algorithm, namely SFS (structural feature set). Some new statistical measures were designed to extract structural features from the direction-lines, such as weighted mean, length-width ratio, and standard deviation, in order to overcome the inadequacy of the PSI. Afterwards, some dimension reduction approaches were employed in order to reduce information redundancy. BPNN, EM-PNN and SVM were used to process the hybrid spectral-structural features after the steps of spatial feature extraction and dimension reduction. The proposed SFS approach was evaluated using two QuickBird datasets and the HYDICE Washington dataset. The results revealed that the new set of reduced spatial features had better performance than PSI.
With respect to the object-based analysis, an adaptive mean shift analysis framework was proposed for object extraction and classification of VHR imagery. The basic idea is to apply a mean shift to obtain an object-oriented representation of VHR data and then use support vector machine to interpret the feature set. In order to employ mean shift effectively, two bandwidth selection algorithms were proposed for the mean shift procedure. One is based on the local structure and the other exploits separability analysis. Experiments were conducted on two VHR datasets, the DC Mall HYDICE image and the Purdue campus HYMAP image. We evaluated and compared the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely the derivative of the morphological profile (DMP). Experimental results verified that the proposed mean shift-based analysis system was robust and obviously outperformed other methods.
The proposed textural, shape and object-based features are extended to multiscale approaches. We aim to 1) extract multiscale features from VHR urban imagery and 2) integrate the multiscale information using three approaches: vector-stacking (VS) SVM, Fuzzy SVM and multi-classifier voting. The VS SVM concatenates the multilevel features in a single SVM. The fuzzy approach deals with the SVM function values for each scale and then chooses the optimal scale according to the membership value. In experiments, three VHR datasets were used for validation of the presented multiscale fusion schemes:ROSIS Pavia datasets and the HYDICE DC Mall image. Experiments showed that both vector-stacking and fuzzy approaches were able to give comparable or higher results than that one obtained by the optimal scale in terms of accuracies. In most cases, the VS SVM provided higher accuracies than the Fuzzy SVM and multi-classifier voting.
At last, we used three case studies to validate and test the multiscale features and the classification methods proposed in this paper. The applications involved 1) the feature analysis and information fusion using aerial photograph and LiDAR data, 2) the mangrove mapping from IKONOS multispectral imagery using multiscale texture information in a study area on the Caribbean coast of Panama, and 3) detailed agriculture mapping using multiscale spectral-textural method based on the PHI airborne data in Xiaqiao, Jiangsu Province.
引文
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