黄为, 李永刚, 汪毅, 张龙. 基于空间共现核的遥感影像分类[J]. 武汉大学学报 ( 信息科学版), 2017, 42(7): 884-889. DOI: 10.13203/j.whugis20150099
引用本文: 黄为, 李永刚, 汪毅, 张龙. 基于空间共现核的遥感影像分类[J]. 武汉大学学报 ( 信息科学版), 2017, 42(7): 884-889. DOI: 10.13203/j.whugis20150099
HUANG Wei, LI Yonggang, WANG Yi, ZHANG Long. Spatial Co-occurrence Kernel Based Aerial Image Classification[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 884-889. DOI: 10.13203/j.whugis20150099
Citation: HUANG Wei, LI Yonggang, WANG Yi, ZHANG Long. Spatial Co-occurrence Kernel Based Aerial Image Classification[J]. Geomatics and Information Science of Wuhan University, 2017, 42(7): 884-889. DOI: 10.13203/j.whugis20150099

基于空间共现核的遥感影像分类

Spatial Co-occurrence Kernel Based Aerial Image Classification

  • 摘要: 采用了基于模糊关系的图像空间共现核来进行高分辨率遥感影像的分类。首先分析了遥感影像的特点,指出其不存在绝对的参考帧。针对该特点,采用了具有较好方向不变性的描述子MROGH(multi-support region order-based gradient histogram)来进行遥感影像底层特征的描述;随后针对图像编码时的软编码情形采用了基于模糊关系的图像空间共现核来构建遥感影像的全局特征汇聚。在公用基准数据集上进行的遥感影像分类实验表明,采用本文方法得到的分类正确率显著优于传统的方式。此外,针对遥感影像分类时采用的不同分类框架进行了评估。

     

    Abstract: We propose to use fuzzy relation based co-occurrence kernel for classification of high-resolution aerial images. By analyzing the characteristics of aerial images, it points out that the imagery does not have an absolute reference frame. For this reason, it uses a local descriptor called MROGH which is inherently rotation invariant to extract low-level features of aerial images. It then uses fuzzy relation based spatial co-occurrence kernel to build the holistic representation of aerial images. Experiments results on publicly available aerial scene imagery dataset show that our method gets a better classification result. In addition, we make a consistent comparative analysis of different classification frameworks based on aerial image dataset.

     

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