Abstract:

In order to promote the development of computer vision technology and improve the utilization of remote sensing image information， the proposed method proposes fine-grained intelligent recognition of sensitive small targets in complex background remote sensing images. The proposed method first utilizes a median filtering algorithm to remove interference noise from the original remote sensing image. After graying out the image， a reasonable selection of background segmentation threshold is used to achieve the division of complex backgrounds and recognition targets in the remote sensing image， avoiding the impact of background information in the remote sensing image on the accuracy of subsequent target recognition. Input the processed remote sensing image into the CNN network， and use spatial selection method to refine and extract the main information features in the remote sensing image based on the output of the network convolutional layer feature map， obtaining fine-grained features of the remote sensing image for sensitive small target recognition； Construct an ELM small target recognition model based on the ELM classification mechanism to achieve fine-grained recognition of sensitive small targets in remote sensing images. Experiments have shown that the proposed method can achieve precise recognition of specific small targets in remote sensing images containing multiple categories of subjects， effectively improving the utilization efficiency of information in remote sensing images， and has significant significance for obtaining important intelligence.

Key words: median filtering algorithm, grayscale processing, cnn network, elm classification mechanism