Abstract: Considering the large-scale and high-dimensional features of remote sensing images, the complex remote sensing application process requires appropriate feature extraction and selection, while further distinguishing different subcategories of similar targets. Therefore, an improved ResNet(Residual Network) algorithm is proposed for fine-grained recognition of complex remote sensing background targets. Non mean filtering algorithm is used to label the coordinate domain of noisy remote sensing images, calculate the similarity between pixels, and denoise complex remote sensing images. Based on the denoising results, global and local feature points of the image are extracted, and global and local feature maps are obtained through feature point fusion results. An improved residual network algorithm is introduced to analyze the fine-grained pixel size of each background image block area. After residual learning, combined with the image pixel position and loss function, a classifier is used twice to determine the fine-grained pixel features and complete the fine-grained recognition of background targets. The experimental results show that the image clarity is high, and as the number of images to be recognized continues to increase, F1 -Score and global recall rates have been improved to various degrees, with lower gain errors. 

Key words: improved ResNet algorithm, complex remote sensing images, image denoising, background objectives, fine grained recognition