基于SVM的图像纹理特征分类研究

支持向量机（SVM）是一种表现卓越的分类方法,而灰度共生矩阵（GLCM）则是一种很好的纹理分析方法,故而本文提出了一种使用灰度共生矩阵进行特征提取的应用支持向量机的纹理特征分类法。实验结果表明,与直接应用灰度信息进行分类的支持向量机算法相比,本文方法可以取得更为准确的分类结果。     

基于支持向量机技术的粮虫图像分类技术研究

介绍了支持向量机的基本思想,提出了一个基于支持向量机的粮虫模式识别系统。该系统先对粮虫图像进行小波边缘提取,根据灰度共生矩阵和局部统计方法提取小波分割后的图像纹理特征。最后利用支持向量机对粮虫图像进行分类。     

基于纹理和边缘的SAR图像多维SVM回归

合成孔径雷达（SAR）图像ROI（Region of Interest）区域存在两种情况：只包含单一地物或由混合地物组成。对此,提出一种基于特征映射的SAR图像多维输出回归方法,该方法不仅能够对只包含单一地物的SAR图像进行类别判断,也能对混合地物区域的内容做出估计。首先对SAR图像提取基于灰度共生矩阵的纹理特征,然后构造了一组能够反映SAR图像边缘长度、方向和稀疏程度的边缘特征向量,最后利用纹理特征和边缘特征对SAR图像进行基于近似迭代变权最小二乘法（IRWLS）的多维支持向量机（Support Vector Machine,SVM）回归。实验结果表明,该方法能够对包含不同地物内容的ROI区域进行有效解译,正确率高。     

基于K型支持向量机的遥感图像分类新算法

为了提高遥感图像的分类精度和识别速度,提出了一种基于K型支持向量机（SVM）的遥感图像分类新算法,该算法将灰度共生矩阵提取的纹理特征与光谱特征相结合进行分类。对两组Landsat ETM+数据进行分类仿真实验,结果表明,在多光谱遥感图像的分类中,新算法提高了分类效率、分类精度和泛化能力,K型SVM是一种优于径向基函数SVM的分类器。     

结合多特征和SVM的SAR图像分割

为实现灰度共生矩阵(GLCM)多尺度、多方向的纹理特征提取, 提出了一种结合非下采样轮廓变换(NSCT)和GLCM的纹理特征提取方法。先用NSCT对合成孔径雷达(SAR)图像进行多尺度、多方向分解; 再对得到的子带图像使用GLCM提取灰度共生量; 然后对提取的灰度共生量进行相关性分析, 去除冗余特征量, 并将其与灰度特征构成多特征矢量; 最后, 充分利用支持向量机(SVM)在小样本数据库和泛化能力方面的优势, 由SVM完成多特征矢量的划分, 实现SAR图像分割。实验结果表明, 基于NSCT域的GLCM纹理提取方法和多特征融合用于SAR图像分割, 可以提高分割准确率, 获得较好的边缘保持效果。     

基于SVM和纹理的煤和煤矸石自动识别

为提高煤矸石的自动识别和分选效率,提出了基于支持向量机(SVM)和纹理识别煤矸石的方法.选取两种煤和一种煤矸石的图像作为样本,经过图像预处理及图像灰度和纹理特征分析后,发现灰度均值、灰度共生矩阵最大值、二阶矩、对比度、相关、熵为有效特征.在此基础上,采用了支持向量机来完成图像的自动识别过程,选取上述6个参数作为支持向量机的训练特征,实验结果表明,该支持向量机识别煤和煤矸石的成功率较高.     

.基于纹理和边缘的SAR图像SVM分类*

为实现SAR图像地物目标的有效分类,深入研究了基于灰度共生矩阵GLCM的四种纹理特征以及两个边缘特征。分析每个单独纹理或边缘特征在对SAR图像进行支持向量机SVM分类中对不同地物的分辨能力,选取不同的特征组合进行组合特征的SVM分类实验。对各种特征进行主成分分析PCA,并考察使用和不使用PCA两种情况下分类结果之间的差异。实验结果证明能量、边缘长度、对比度和相关度的特征组合在PCA作用下能够改善各类地物的分类精度,将总分类精度提高到90%以上。     

基于纹理和BP神经网络的SAR图像分类

研究基于纹理和BP神经网络的SAR图像分类。首先用增强FROST滤波算法对SAR图像进行去噪处理。然后基于灰度共生矩阵理论提取去噪后的SAR图像多种纹理特征,并通过大量实验筛选出有效的纹理特征。最后,结合纹理特征,分别采用经典的最大似然分类法和BP神经网络分类法对SAR图像进行分类。实验结果表明：纹理信息辅助SAR图像的灰度进行分类,大大地提高了SAR图像的分类精度;基于BP神经网络的SAR图像分类精度高于最大似然分类法的分类精度。     

灰度共生矩阵纹理特征提取的Matlab实现

图像的特征提取是图像的识别和分类、基于内容的图像检索、图像数据挖掘等研究内容的基础性工作,其中图像的纹理特征对描述图像内容具有重要意义,纹理特征提取已成为目前图像领域研究的热点。文中深入研究了基于灰度共生矩阵（GLCM）的纹理特征提取方法,给出了基于Matlab的简便实现代码,分析了共生矩阵各个构造参数对构造共生矩阵的影响。分析结果对优化灰度共生矩阵的构造、实现基于灰度共生矩阵（GLCM）的特定图像的纹理特征提取等都具有重要参考意义。     

基手小波变换和支持向量机的遥感图像目标检测

探讨基于支持向量机的高分辨率遥感图像中某型号飞机的检测识别问题.提出将小波变换结合灰度共生矩阵法提取目标样本信息特征的一种新方法,通过对Brodatz纹理进行测试,实验表明该方法有效提高了纹理分类识别率.此外,将支持向量机方法运用于遥感图像目标识别中,用分块区域搜索的方法检测到目标所在区域,实现对目标的检测识别.试验表明,该方法快速、高效且具备一定的鲁棒性.     

Classification of MODIS images combining surface temperature and texture features using the Support Vector Machine method for estimation of the extent of sea ice in the frozen Bohai Bay,China

Image classification of frozen areas and adjacent sea ice is important for monitoring the evolution of ocean freezing. This paper proposes a novel approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) image classification and estimation of the extent of sea ice in frozen areas during recent global surface warming hiatus. We derived the texture feature (TF) and surface temperature (ST) from the MODIS image for classification and sea ice detection. We extracted MODIS TF by a grey-level co-occurrence matrix (GLCM), and retrieved the MODIS ST using a split-window method, and finally classified the image using a Support Vector Machine (SVM) convoluting the ST and TF methods. Results were compared and validated with those of conventional spectral-based supervised classification approaches. Results show that the overall accuracy and kappa coefficient (κ) using the proposed method was much higher in comparison with those of the spectral-based maximum likelihood and SVM methods. The SVM fusion ST and TF method was effective and useful for MODIS 500 m image classification and sea ice mapping in frozen area. Combining ST and TF can improve sea ice extent estimation accuracy in the frozen Bohai Bay.     

Liver fibrosis staging using CT image texture analysis and soft computing

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons.     

基于纹理特征提取的图像分类方法研究及系统实现*

深入研究灰度共生矩阵算法,结合和差统计法对其进行改进。编码实现改进的图像纹理提取算法,并采用基于径向基内积函数内核的支持向量机方法对图像分类效果进行实验。通过训练和测试证明,该系统能减少特征提取的计算时间和存储空间,并可达到良好的图像分类效果     

Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis

The long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, including eight texture features from the Grey-Level Co-occurrence Matrix (GLCM) method; a computationally efficient texture feature, the Number of Different Grey-levels (NDG); and a structural texture feature, Edge Density (ED), were evaluated. It was found that generally single texture features performed poorly. Classification accuracy increased with increasing number of texture features until three or four texture features were combined. The more texture features in the combination, the smaller difference between different combinations. The results also show that a lower number of texture features were needed for more homogeneous areas. NDG and ED combined with GLCM texture features produced similar results as the same number of GLCM texture features. Two classification schemes were adopted, stratified classification and non-stratified classification. The best stratified classification result was better than the best non-stratified classification result.     

Three robust features extraction approaches for facial gender classification

This research paper introduces three robust approaches for features extraction for gender classification. The first approach is based on using Discrete Cosine Transform (DCT) and consists of two different methods for calculating features values. The second approach is based on the extraction of texture features using the gray-level cooccurrence matrix (GLCM). The third approach is based on 2D-wavelet transform. The extracted features vectors are classified using SVM. For precise evaluation, the databases used for gender evaluation are based on images from the AT@T, Faces94, UMIST, and color FERET databases. K-fold cross validation is used in training the SVM. The accuracies of gender classification when using one of the two proposed DCT methods for features extraction are 98.6 %, 99.97 %, 99.90 %, and 93.3 % with 2-fold cross validation, and 98.93 %, 100 %, 99.9 %, and 92.18 % with 5-fold cross validation. The accuracies of GLCM texture features approach for facial gender classification are 98.8 %, 99.6 %, 100 %, and 93.11 %, for AT@T, Faces94, UMIST, and FERET, databases. The accuracies for all databases when using 2D-WT are ranging between 96.18 % and 99.6 % except FERET and its accuracy is 92 %.     

Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery

Textural features of high-resolution remote sensing imagery are a powerful data source for improving classification accuracy because using only spectral information is not sufficient for the classification of objects with within-field spectral variability. This study presents the methods of using an object-oriented texture analysis algorithm for improving high-resolution remote sensing imagery classification, including wavelet packet transform texture analysis, the grey-level co-occurrence matrix (GLCM) and local spatial statistics. Wavelet packet transform texture analysis, with the method of optimization and selection of wavelet texture for feature extraction, is a good candidate for object-oriented classification. Feature optimization is used to reduce the data dimensions in combinations of textural sub-bands and spectral bands. The result of the classification accuracy assessment indicates the improvement of texture analysis for object-oriented classification in this study. Compared with the traditional method that uses only spectral bands, the combination of GLCM homogeneity and spectral bands increases the overall accuracy from 0.7431 to 0.9192. Furthermore, wavelet packet transform texture analysis is the optimal method, increasing the overall accuracy to 0.9216 using a smaller data dimension. Local spatial statistical measures also increase the classification total accuracy, but only from 0.7431 to 0.8088. This study demonstrates that wavelet packet and statistical textures can be used to improve object-oriented classification; specifically, the texture analysis based on the multiscale wavelet packet transform is optimal for increasing the classification accuracy using a smaller data dimension.     

Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city,Uttar Pradesh

Investigations have been carried out for digital spectral and textural classification of an Indian urban environment using SPOT images with grey level co-occurrence matrix (GLCM), grey level difference histogram (GLDH), and sum and difference histogram (SADH) approaches. The results indicate that a combination of texture and spectral features significantly improves the classification accuracy compared with classification with pure spectral features only. This improvement is about 9% and 17% for an addition of one and two texture features, respectively. GLDH and SADH give statistically similar results to GLCM, and take less computing time than GLCM. Conventional separability measures like transformed divergence, Bhattacharya distance, etc. are not effective in feature selection when classification is carried out with spectral and texture features. An alternative approach using simple statistics such as average coefficient of variation, skewness, and kurtosis and correlation amongst feature sets has shown greater feature selection potential when a combination of spectral and texture features is used.     

综合灰度与纹理特征的高分辨率星载SAR图像建筑区提取方法研究

高分辨率SAR图像的纹理特性对于图像的解译及地物分类等具有重要的意义。根据高分辨率星载SAR图像上建筑区的纹理有别于其他地物的特点,提出了一种综合利用灰度和纹理特征的高分辨率星载SAR图像建筑区提取方法。首先对SAR图像进行斑点噪声的抑制,然后利用灰度共生矩阵计算出星载SAR图像上建筑区与非建筑区的8种纹理特征统计量,根据巴氏距离进行特征选择,并通过主成分分析去除纹理特征之间的相关性,得到了最佳纹理特征分量,将所选的特征影像与原始图像进行波段组合,利用K均值聚类算法对组合后的图像进行非监督分类;最后通过对分类图像进行后处理并提取外部轮廓,提取了建筑区。以COSMO-SkyMed SAR影像为数据源进行了实验。结果表明该方法能够有效提取高分辨率星载SAR图像中的建筑区,提取效果明显优于未利用纹理特征的方法。