| 综合聚类和上下文特征的高光谱影像分类 |
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| 引用本文： | 鲍蕊,薛朝辉,张像源,苏红军,杜培军. 综合聚类和上下文特征的高光谱影像分类[J]. 武汉大学学报(信息科学版), 2017, 42(7): 890-896. DOI: 10.13203/j.whugis20150043 |
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| 作者姓名： | 鲍蕊 薛朝辉 张像源 苏红军 杜培军 |
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| 作者单位： | 1.天津市地质调查研究院, 天津, 300191 |
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| 基金项目： | 江苏省杰出青-基金BK2012018国家重大科学仪器设备开发专项012YQ050250 |
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| 摘 要： | 常规高光谱影像逐像素分类往往没有考虑空间相关性，分类结果未体现地物的空间关联和分布特征。为了在分类中充分利用空间特征，利用聚类信息并结合隐马尔可夫随机场模型讨论了高光谱遥感影像光谱-空间分类方法。首先，在不同特征提取方法（最小噪声分离、独立成分分析和主成分分析）下，使用不同聚类方法（k-均值、迭代自组织分析算法和模糊c-均值算法）借助隐马尔可夫随机场获取优化的分割图；然后，采用4连通区域标记法对分割区域标记生成图像对象，并根据支持向量机的逐像素分类结果采用多数投票法对图像对象进行分类；最后，借助凹槽窗口邻域滤波技术改进分类结果，削弱“椒盐”现象。该方法综合了监督分类和非监督分类的优势，通过聚类引入地物空间相关性信息，通过隐马尔可夫随机场引入上下文特征，较好地弥补了单纯基于光谱信息分类的不足。
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| 关 键 词： | 聚类 隐马尔可夫随机场 支持向量机 高光谱影像 光谱-空间分类 多数投票 |
| 收稿时间： | 2015-09-18 |
Classification Merged with Clustering and Context for Hyperspectral Imagery |
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| Affiliation: | 1.Tianjin Institute of Geological Survey, Tianjin 300191, China2.Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China3.Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China4.School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China |
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| Abstract: | The traditional pixel-wised classification methods for hyperspectral image (HIS) only consider spectral information while ignoring the spatial information, resulting in a big limit of classification performance. Clustering which could assemble pixels similar in spectral features into spatial adjacent clusters, thus effectively express similarity and spatial correlation of adjacent pixels. In order to take full advantages of spatial correlation, this paper explore a spectral-spatial classification method for HSI merged with clustering and context. Firstly, under condition of different feature extraction(MNF, ICA and PCA), different clustering methods(k-means, ISODATA and FCM) are used in hidden markov random field to obtain optimized segmentation map containing context features; secondly, the regions in the segmentation map are labeled by using a four-connected neighborhood labeling method to generate image objects, and a majority voting method is used to classify the objects based on the initial classification map derived from support vector machine (SVM) optimized by particle swarm optimization (PSO). Finally, a Chamfer neighborhood filtering technique is used to regularize the classification map, which partially reduces the noise. This method utilizing spatial information from clustering and introducing context features from HMRF takes advantage of supervised classification and unsupervised classification to gain noise reduction, high-accuracy and high homogeneity, which makes up for the inadequacy of the classification based only on spectral information. Experiment on ROSIS data set and AVIRIS data set respectively illustrate that the method can obtain better performance in terms of classification. The overall accuracy of ROSIS data set reaches to 98.53%, 5.01% higher than that obtained by SVM. Meanwhile the overall accuracy of AVIRIS data set climbs to 91.97%, 7.01% higher than SVM result. We also find that different feature extraction and different clustering will influence the spectral-spatial method using HMRF with edge-protection. |
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