Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors
Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors
复制标题
使用多尺度完整局部二值模式和 Fisher 向量进行遥感图像场景分类
DOI：
10.3390/rs8060483
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发表时间：
2016-06-01
期刊：
影响因子：
5
通讯作者：
Du, Qian
中科院分区：
文献类型：
--
作者：
Huang, Longhui;Chen, Chen;Du, Qian
提出了一种有效的遥感图像场景分类方法，使用基于补丁的多尺度完成的本地二进制图案（MS-CLBP）功能和Fisher Vector（FV）。该方法通过将图像及其多尺度版本划分为密集的补丁并使用CLBP描述符来表征本地旋转不变纹理信息来提取一组本地补丁描述符。然后，Fisher Vector编码用于将局部补丁描述符（即基于贴片的CLBP特征）编码为判别表示。为了提高特征表示的判别能力，使用多组参数用于CLBP生成多个FVS，这些FV被串联为图像的最终表示。然后使用基于内核的极限学习机（KELM）进行分类。在两个公共基准遥感图像数据集（即21级土地使用数据集和19级卫星场景数据集）上对所提出的方法进行了广泛的评估，并导致分类性能卓越的性能（21级数据集的21级数据，而与State-the-art-the-Art Imprester相比，MS-CLBP和94.344.344.32.34.32％的改进率为3％。 1％）。
An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture information. Then, Fisher vector encoding is used to encode the local patch descriptors (i.e., patch-based CLBP features) into a discriminative representation. To improve the discriminative power of feature representation, multiple sets of parameters are used for CLBP to generate multiple FVs that are concatenated as the final representation for an image. A kernel-based extreme learning machine (KELM) is then employed for classification. The proposed method is extensively evaluated on two public benchmark remote sensing image datasets (i.e., the 21-class land-use dataset and the 19-class satellite scene dataset) and leads to superior classification performance (93.00% for the 21-class dataset with an improvement of approximately 3% when compared with the state-of-the-art MS-CLBP and 94.32% for the 19-class dataset with an improvement of approximately 1%).