In stereo vision technology, image matching is one of the most important parts, and the coefficient of correlation matching is recognized to be more mature and stable matching algorithm. Correlation coefficient method has high sensitivity to image rotation, but do not have rotation invariance, and require a large computational complexity. Because of this it cannot be widely applied in the field of real-time image matching. This paper is aimed at this drawback to make its computational complexity greatly reduced, posses the scale and rotation invariance, so as to meet the requirements of real-time image matching system, this paper proposes a image registration algorithm of accurate registration combined with Fourier-Mellin transform and Radon transform of image. After the introduction of Fourier transform and correlation coefficient method to detect the correct rotation factor and scale factor, it is provided a reliable basis for correlation coefficient method of image registration to achieve both rotation and scaling invariance, image using this method is verified by the experiments on the feasibility of the registration, the registration accuracy is improved.
In this paper, a combination method, between the neural network and textures information, is proposed to remote sensing images classification. The methodology involves an extraction of texture features using the gray level co-occurrence matrix and image classification with BP artificial neural network. The combination of texture features and the digital elevation model (DEM) as classified bands to neural network were used to recognized different classes. This scheme shows high recognition accuracy in the classification of remote sensing images. In the experiments, the proposed method was successfully applied to remote sensing image classification and Land Use Change Detection, in the meanwhile, the effectiveness of the proposed method was verified.