地理科学 ›› 2018, Vol. 38 ›› Issue (2): 293-299.doi: 10.13249/j.cnki.sgs.2018.02.016

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基于多重分割关联子的高分辨率遥感场景分类

徐培罡1(), 张海青1, 王超2, 齐岗1, 李杰1, 吴静阳1   

  1. 1.国家测绘地理信息局第一航测遥感院, 陕西 西安 710054
    2.内蒙古自治区基础地理信息中心, 内蒙古 呼和浩特 010010
  • 收稿日期:2017-02-14 修回日期:2017-05-07 出版日期:2018-04-10 发布日期:2018-04-10
  • 作者简介:

    作者简介:徐培罡(1986-),男,陕西渭南人,工程师,主要从事摄影测量与遥感、数字城市及GIS应用开发与研究。E-mail: 370457225@qq.com

  • 基金资助:
    国家自然科学基金项目(41271144)、2013年度河南省政府决策研究招标课题(2013B053)资助

High Resolution Remote Sensing Image Classification Based on Multiple Segmentation Correlograms Model

Peigang Xu1(), Haiqing Zhang1, Chao Wang2, Gang Qi1, Jie Li1, Jingyang Wu1   

  1. 1.The First Institute of Photogrammetry and Remote Sensing, Xi’an 710054, Shaanxi,China;
    2. Geometic Center of Inner Mongolia, Hohhot 010010, Inner Mongolia, China
  • Received:2017-02-14 Revised:2017-05-07 Online:2018-04-10 Published:2018-04-10
  • Supported by:
    National Natural Science Foundation of China (41271144), The 2013 Henan Provincial Government Decision-making Research (2013B053).]

摘要:

高分辨率遥感影像提供了丰富的外观信息和空间结构信息,广泛应用于土地利用分类当中,源于文章领域的视觉词袋(Bag-of-Visual-Words,BoVW)模型现已成功应用于图像分类领域。传统的BoVW模型忽略了特征之间的空间布局信息和像素一致性信息,提出多重分割关联子特征,融合图像的外观信息、空间布局信息和像素一致性信息,实验表明该方法能够获取优于许多经典的遥感图像特征的性能。

关键词: 多重分割, 空间信息, 像素一致性, 高分辨率遥感图像分类

Abstract:

High resolution remote sensing images are increasingly applied in land use classification problems. However, it is a difficult task to recognize the semantic category for the complex background and multiple land-cover classes. The bag-of-visual-words model has been successful in scene classification, but ignores pixel homogeneity in land use remote sensing images. In this article, we present a multiple segmentation-based correlation feature to jointly integrate appearance, spatial correlation, and pixel homogeneity. We use a dense feature representation to detect spurious features resulting from clutter, which has been demonstrated that dense features work better for scene classification. These dense features are Scale-Invariant Feature Transform descriptors using a dense regular grid instead of interest points to extract features. The visual vocabulary is formed by K-means clustering of a random subset of patches from the training set. A multiple segmentation-based correlogram, which is a matrix express spatial co-occurrences of features, encoding both the local and global shape of visual words and robust with respect to basic geometric transformations and occlusions, were extracted. The corresponding correlogram elements for each pair of visual word labels in training images are collected and clustered using K-means. The set of cluster centers are multiple segmentation-based correlatons, which are a set of representative multiple segmentation-based correlogram elements. Therefore, multiple segmentation-based correlatons compress the co-occurrences information contained in a multiple segmentation-based correlogram without loss of discrimination accuracy. Finally, the concatenated histograms of images, which describe the underlying spatial correlation of visual words considering pixel homogeneity in the image region, are used as input feature vectors for the SVM classifier. The effectiveness of the multiple segmentation-based correlaton models was tested on a ground truth image dataset of 21 land use classes manually extracted from high-resolution remote-sensing images. The experimental results demonstrate that our improved correlaton model can promote classification and outperforms existing methods for the jointly integration of appearance, spatial correlation, and pixel homogeneity information.

Key words: multiple scale segmentation, spatial information, pixel homogeneity, scene classification based on high-resolution remote sensing

中图分类号: 

  • P237