Cropland Extraction from Very High Spatial Resolution Satellite Imagery by Object-Based Classification Using Improved Mean Shift and One-Class Support Vector Machines
Cropland Extraction from Very High Spatial Resolution Satellite Imagery by Object-Based Classification Using Improved Mean Shift and One-Class Support Vector Machines
复制标题
使用改进的均值平移和一类支持向量机通过基于对象的分类从极高空间分辨率卫星图像中提取农田
DOI：
10.1166/sl.2011.1361
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发表时间：
2011-06
期刊：
影响因子：
--
通讯作者：
Xu, Shenghua
中科院分区：
文献类型：
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作者：
Shen, Jing;Liu, Jiping;Lin, Xiangguo;Zhao, Rong;Xu, Shenghua
从甚高空间分辨率（VHR）卫星图像中提取农田的问题仍然是一个巨大的挑战。提出了一种基于改进均值漂移和单类支持向量机的VHR卫星影像农田目标分类方法。将融合后的卫星图像进行非负矩阵分解，得到三个波段，然后利用改进的均值漂移算法进行图像分割。随后，检测分割图像中的每个区域的结构线，并且计算直线的方向的标准偏差。选择光谱信息和以上导出的纹理信息作为用于以下分类的特征。最后，利用支持向量数据描述方法对分割后的图像进行农田识别。采用3幅不同空间分辨率的卫星影像对该算法进行测试，结果表明，该方法的整体分类精度高于eCognition方法，且随着空间分辨率的提高，其整体分类精度也随之提高。该方法的另一个优点是只需要农田样本，节省了时间和成本。
The issue of cropland extraction from very high spatial resolution (VHR) satellite imagery remains a great challenge. In this paper, an object-based classification method for cropland extraction from VHR satellite imagery is proposed based on the improved mean shift and one-class SVM. After the fused satellite image is transformed by nonnegative matrix factorization into three bands, the improved mean shift is employed to segment the image. Subsequently, the structure lines of each region in the segmented image are detected, and the standard deviations of the directions of the straight lines are calculated. The spectral information and the above derived texture information are selected as features for the following classification. At last, the support vector data description is utilized to recognize the croplands from the segmented image based on only some cropland samples. Three satellite images with different spatial resolutions are employed to test the algorithm, and the results show that our proposed method obtains a higher overall classification accuracy than the eCognition's method does, and its overall classification accuracy is promoted with the increasing of spatial resolution. Another merit of our method is that it needs only the cropland samples, which is time-saving and cost-saving.