Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator

Road edge detection from remote sensing images, as an important ground object type, plays an important role in people's life and travel and urban planning and development, and extracting road information from remote sensing images has practical scientific value and practical significance. However, with the development of remote sensing technology, while the resolution of remote sensing images is improved, the information describing ground objects becomes more and more abundant, and the difficulty of identifying and extracting road information is also increased. In the process of acquiring remote sensing images, the actual system is subjected to various kinds of noise interference. Different environmental interference and system defects will introduce noises with completely different distribution and statistical characteristics to remote sensing images. Aiming at the problem that the detection effect of traditional algorithms becomes worse due to the influence of noise on remote sensing images, a wavelet transform denoising method and morphological gradient operator are proposed. By selecting appropriate structural elements of remote sensing images, noise pixels cannot participate in morphological calculation, and the noise intensity changes with the size of quantum superposition state structural elements. Therefore, a morphological gradient operator is established and applied to edge detection of remote sensing images. Finally, the experimental results show that the method proposed in this article is better than other directions in terms of effect through road edge detection and matching. This method can effectively reduce noise. Compared with other algorithms, the method proposed in this article has certain advantages.