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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Object Classification of Aerial Images With Bag-of-Visual Words
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Object Classification of Aerial Images With Bag-of-Visual Words

机译：带视觉袋的航空影像的对象分类

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摘要

This letter presents a Bag-of-Visual Words (BOV) representation for object-based classification in land-use/cover mapping of high spatial resolution aerial photograph. The method is introduced to handle the special characteristics of aerial images, i.e., variability of spectral and spatial content. Specifically, patch detection and description are used to divide and represent various subregions of objects comprising multiple homogeneous components. Moreover, the BOV representation is constructed with the statistics of the occurrence of visual words, which are learned from the training data set. A combination of spectral and texture features is verified to be a satisfactory choice through the evaluations of various patch descriptors. Furthermore, a threshold-based method is employed to reduce the impact of outliers on classification in test data. Experiments based on aerial-image data set show that the proposed BOV representation yields better classification performance than the low-level features, such as the spectral and texture features.
机译：这封信提出了视觉袋（BOV）表示法，用于在高分辨率空间照片的土地使用/覆盖图绘制中基于对象的分类。引入该方法来处理航空图像的特殊特征，即光谱和空间内容的可变性。具体地，斑块检测和描述用于划分并表示包括多个同质成分的物体的各个子区域。此外，BOV表示是根据从训练数据集中获悉的视觉单词的出现统计数据构建的。通过评估各种补丁描述符，可以证明光谱和纹理特征的组合是令人满意的选择。此外，采用基于阈值的方法来减少离群值对测试数据分类的影响。基于航拍图像数据集的实验表明，与低层特征（例如光谱和纹理特征）相比，所提出的BOV表示具有更好的分类性能。

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