越野行车环境中的地表分类方法研究
毕业论文关键词 ALV 地表分类 稀疏表示 KSVD
Title Some Research on Land Cover Classification Method in Cross-country Environment
Abstract Autonomous Land Vehicle,which is also called Smart Car,is an important application of wheeled robots in the transport sector.All kinds of sensors such as camera,laser radar and GPS have been used to help the robot sensing the environment around，the robot then can drive automaticlly.Therefore,a robust road environment sensing technology is the foundation of ALV. The purpos of environment sensing is to classify all kinds of land cover correctly from nature scene. Our project takes the Outex texture database and the land cover database which is created by Nanjing University of Sciene and Technology as the data source. We then extract the color ,LBP ,GLCM ,Gabor feature from images to build feature space. Two methods for dictionary learning in the model of sparse representation are reseached based on the feature space. The first method is to learn a dictionary for each class with KSVD. Learning a label consistent dictionary by KSVD framework is the second way. Some experiments for land cover classification on the dataset have been done, the results show that color feature contributes more to the classification of land cover and the classification accuracy of Label consistent dictionary learning method can up to 97% when the feature space has low dimension.
Keywords ALV Land Cover Classification Sparse Representation KSVD
目次
1引言1
1.1越野行车环境中地表覆盖物分类问题综述1
1.2基于稀疏表示模型的地表分类问题框架3
2图像的常用特征提取算法5
2.1颜色矩（ColorMoments）特征5
2.2局部二进制模式(LBP)特征6
2.3灰度共生矩阵(GLCM)7
2.4Gabor纹理特征9
2.5特征分类性能的比较10
3稀疏表示模型中的字典学习方法13
3.1稀疏模型用于分类的基础13
3.2直接利用样本构建字典用于稀疏分类(N_SRC)15
3.3KSVD学习字典用于稀疏分类（KSVD_SRC）16
3.4标签一致性KSVD字典学习方法用于分类(LCKSVD_SRC)17
4实验结果21
4.1Njust_64数据库上的单特征分类对比实验21
4.2所有数据库上的全特征分类对比实验21
结论23
致谢24
参考文献25
1 引言 本课题来源于自主车项目的环境感知子课题，本章将首先对越野行车环境中地表覆盖物分类问题进行综述，继而提出本课题的解决思路和系统框架。
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