资源科学 ›› 2017, Vol. 39 ›› Issue (3): 557-565.doi: 10.18402/resci.2017.03.17

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基于多特征软概率级联的土地利用/土地覆盖分类

张斌(), 刘越岩, 汪林宇   

  1. 中国地质大学土地资源管理系,武汉 430074
  • 收稿日期:2016-09-20 修回日期:2017-02-05 出版日期:2017-03-20 发布日期:2017-03-20
  • 作者简介:

    作者简介:张斌,男,湖南益阳人,博士,讲师,硕士生导师,主要从事高分辨率遥感影像土地利用分类。E-mail:zhangbin@cug.edu.cn

  • 基金资助:
    国家自然科学基金资助项目(41601480);对地观测技术国家测绘地理信息局重点实验室开放基金(K201407 )

Land use and cover classification based on multi-feature soft probability cascading

Bin ZHANG(), Yueyan LIU, Linyu WANG   

  1. China University of Geosciences,Department of Land Resources Management,Wuhan 430074,China
  • Received:2016-09-20 Revised:2017-02-05 Online:2017-03-20 Published:2017-03-20

摘要:

为实现高分辨率遥感影像低层特征的有效组织与优化,提高特征的可判别性,重点研究了基于稀疏编码的中层特征学习、基于支持向量机(Support Vector Machine,SVM)的分类技术,提出了基于软概率级联中层特征学习模型实现土地利用/土地覆盖(Land Use and Land Cover,LULC)分类。首先,提取影像的灰度共生矩阵(Gray-level Co-occurrence Matrix,GLCM)、光谱特征、密集尺度不变特征转换(Dense Scale Invariant Feature Transform,DSIFT)作为低层特征;然后由稀疏编码分别对GLCM、DSIFT和光谱特征进行稀疏编码,并结合最大平滑方法对稀疏系数进行优化,获得影像的中层特征,并通过SVM分类器分别计算LULC类别软概率,对其级联获得影像的特征表达;最后,利用SVM分类器再次分类获得LULC分类结果。选用武汉市远城区农村居民点作为实验样区,对该方法进行了验证,实验结果表明,该方法总体精度达到88%左右;相较于提取单一低层特征的分类方法,本文算法可有效提高LULC分类精度。

关键词: 高分辨率, 遥感影像, 图像分类, 土地利用/土地覆盖, 稀疏编码, 支持向量机

Abstract:

In order to realize the effective organization and optimization of low-level features for high resolution remote sensing images,and make feature descriptors more discriminative,we focused on mid-level feature learning based on sparse coding and support vector machine (SVM)classification,and propose a land use / land cover (LULC)classification method based on soft probability cascading and mid-level feature learning model. First,the gray level co-occurrence matrix (GLCM),Dense Scale-Invariant Feature Transform (DSIFT)and spectral feature descriptors are extracted as low-level feature descriptors. Second,sparse coding is adopted to obtain the sparse coefficients of GLCM,DSIFT and spectral features,and then max-pooling methods are used for learning mid-level feature descriptors. Combined with SVM classification with training samples of LULC class types the soft probabilities of different LULC class types are calculated. Three different kinds of soft probabilities belong to each LULC class type,which takes GLCM,DSIFT and spectral features as low-level feature descriptors respectively,and are cascaded for the construction of final feature descriptors. The cascaded feature descriptors are more discriminative than unsupervised mid-level feature descriptors,because it is learned by a supervised way. This method incorporates different low-level feature descriptors effectively. Finally,using the cascaded feature descriptors,the LULC classification map is achieved by the SVM classifier in a supervised way. Taking rural residents in the district area of Wuhan City as an experimental area,our proposed method was verified by aerial high resolution remote sensing images. Experimental results show that the overall accuracy is 88%. Compared with the extraction of a single low-level feature classification method,the algorithm in this paper can effectively improve LULC classification accuracy.

Key words: high-resolution, remote sensing images, image classification, land use and land cover, sparse coding, support vector machine