Texture Extension Method for Farmland Remote Sensing Image Based on Shannon-cosine Wavelet

Remote sensing images are generally large images. For the subsequent analysis of this kind of image, there is a common method of dividing image into blocks, while the boundary effect is easy to occur in block processing. Therefore, the elimination of boundary effects is a problem that needs to be studied in block processing. The most common way to eliminate the boundary effects is to extend the image. Symmetry extension, zero extension and periodic extension are the common extension methods. The conventional extension method is not applicable because texture in farm remote sensing images carries important information. Thus, according to the line characteristics shown on remote sensing images, a new extension method based on texture orientation was proposed in this paper. Here, we used the method of multi-scale interpolation wavelet to solve the partial differential equation, according to the change of gray level of the image. In this method, external collocation points were chosen adaptively. Thus the computational efficiency could be greatly improved. Then, the texture direction of farm remote sensing images was identified by using bounding boxes, and the texture is further extended along the texture direction. Experimental results show that the image extension method proposed effectively overcomes the shortcomings of the conventional extension method, greatly improve the efficiency of calculation and the boundary effect is effectively eliminated. © 2017, Chinese Society of Agricultural Machinery. All right reserved.