视觉词袋和Gabor纹理融合的遥感图像检索

Remote Sensing Image Retrieval Based on the Fusion of Bo VW and Gabor Texture

针对高分辨率的遥感图像,提出了一种视觉词袋和Gabor纹理融合的图像检索方法。遥感图像纹理信息丰富,局部关键点多,当图像存在较多相似纹理时,视觉词袋检索准确率下降。将视觉词袋和Gabor纹理融合在一起结合了局部特征和全局特征以及中层词袋和底层纹理的优点,可以改进遥感图像的描述方式。实验结果表明,通过合理地分配视觉词袋和Gabor纹理的权重,特征融合的检索性能与单一特征方法相比有较大提高,并优于传统的Gabor纹理和颜色矩融合方法。因此,视觉词袋和Gabor纹理融合在遥感图像检索领域是一种有效的方法。

A retrieval method based on the fusion of Bag of Visual Words （BoVW） and Gabor texture is presented for the high resolution remote sensing images. Remote sensing images have rich texture information and many local key points But when an image contains lots of similar texture, the retrieval precision of BoVW will be reduced. The fusion of BoVW and Gabor texture combines the advantages of local feature and global feature, mid-level feature and low-level texture to improve image description. Experiment results show that the presented fusion method is superior to the traditional fusion method using Gabor texture and color moments. Retrieval performance of the fused features method is improved compared with that using single feature, and the improved performance depended on the suitable fusion weights Experiment results indicate that the fused BoVW and Gabor texture is effective for high-resolution remote sensing image retrieval