| 适用于遥感分类的多邻域粗糙集加权特征提取方法 |
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| 引用本文： | 陈铁桥,柳稼航,朱锋,王一豪,刘佳,陈杰.适用于遥感分类的多邻域粗糙集加权特征提取方法[J].武汉大学学报(信息科学版),2018,43(2):311-317. |
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| 作者姓名： | 陈铁桥 柳稼航 朱锋 王一豪 刘佳 陈杰 |
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| 作者单位： | 1.中国科学院西安光学精密机械研究所, 陕西 西安, 710119 |
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| 基金项目： | 国家重点研发计划2016YFF0103604西部青-学者计划A类XAB2015A07国家重点基础研究发展计划（973计划）2012CB719906 |
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| 摘 要： | 邻域粗糙集是一种有效的影像特征提取方法，邻域粗糙集模型存在稳定性不高和邻域半径需要反复调整的不足，难以实现地物特征的自动化提取。提出一种多邻域粗糙集加权特征提取方法用于高分辨率遥感影像特征提取。该方法首先利用不同半径的邻域粗糙集对影像的光谱和纹理特征进行提取，求得不同邻域半径下的有效特征子集；然后统计所有邻域半径下各个特征出现的概率，将概率作为权重与特征进行加权得到最终地物特征。QucikBird影像上分类试验表明本文算法优于传统邻域粗糙集特征提取方法，分类总精度平均提高3.88%，Kappa系数平均提高5.16%。在GeoEye-1影像上的分类试验同样证明了本文方法的有效性。
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| 关 键 词： | 邻域粗糙集模型 多邻域粗糙集 特征提取 高分辨率遥感影像 面向对象分类 |
| 收稿时间： | 2017-09-05 |
A Novel Multi-radius Neighborhood Rough Set Weighted Feature Extraction Method for Remote Sensing Image Classification |
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| Institution: | 1.Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China2.University of Chinese Academy of Sciences, Beijing 100049, China3.Key Laboratory of Lunar and Deep Space Exploration, Beijing 100012, China4.Department of Geo-informatics, Central South University, Changsha 410083, China |
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| Abstract: | The neighborhood rough set model can be effective for keeping or even improving classification accuracy. This model however, still has some disadvantages as it has low stability in classification precision, requires repeated neighborhood radius adjustments, and cannot realize automatic feature extraction. In order to solve these problems, this paper presents a multi-radius neighborhood rough set weighted feature extraction method for high resolution remote sensing image classification. The neighborhood rough set model was used to extract texture and spectrum features of image by setting gradually increasing radius with equally spaced steps, as a result effective subset features under different radius were obtained. The presence probablity of each feature under all the different radius was calculated, each feature was endowed with weight by its presence probability, so the final weighted features of image were acquired. The newly obtained features were applied to image classification using a support vector machine. Experiments on QuickBird images demonstrate that the proposed method can provide better classification results. Compared with other state-off-art neighborhood rough set model with effective radius, the overall accuracy exceeded about 3.88% while the Kappa coefficient exceeded about 5.16%. A classification experiment on a GeoEye-1 image also showed the effectiveness of the proposed method. All the classification experiment results show that the proposed method can improve classification precision and automation of high resolution remote sensing images. |
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