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。

Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.