Heavy-fog scene aerial image fusion recognition method based on self-adaptive cloud model

Abstract

The invention discloses a heavy-fog scene aerial image fusion recognition method based on a self-adaptive cloud model. The method comprises the following steps of acquiring an original heavy fog sceneaerial image as a sample, and performing graying processing on the heavy fog scene aerial image by using a weighted graying algorithm, and obtaining a grayscale image; processing the grayscale imageby using histogram equalization and obtaining a balanced grayscale image, carrying out edge detection on the balanced grayscale image to obtain an edge image, and initializing a convolution neural network and training data, and extracting feature data; performing multi-scale decomposition on an image subjected to training through a heavy-fog scene aerial image recognition training model and obtaining a smooth image and a detail image, and the image is reconstructed to obtain a fused image, and the feature data extracted by inputting the fused image into the convolutional neural network model are transmitted to a support vector machine for training, and the extracted test characteristic data are input into a large fog scene aerial image recognition training model for judgment, and finally the accurate large-fog scene aerial image recognition result is obtained.

Description

technical field [0001] The invention relates to the technical field of remote sensing, in particular to an adaptive cloud model-based aerial image fusion recognition method for heavy fog scenes. Background technique [0002] At present, image processing refers to preprocessing, registration, fusion, segmentation and other related operations on existing images to obtain a more accurate and reliable image description of the same scene or the same target. [0003] The present invention takes the fog image as the research target, and proposes a new fusion method, so that the fused brain image is more in line with the visual characteristics of human or machine, so as to facilitate further analysis of the image and facilitate the application of clinical medicine. Over the past few decades, image fusion methods have emerged in an endless stream, which can be roughly divided into some three categories: [0004] 1. Fusion method based on space domain; [0005] 2. Fusion method base...

AI Technical Summary

Problems solved by technology

Visibility is greatly reduced in heavy fog images, so it is easy to cause traffic accidents at high speed and heavy fog, so it is more important to provide a technology that can well distinguish the front vision in heavy fog

[0009] At present, the processing and segmentation of images for heavy fog images has low recognition ability due to its complexity and variability, and often leads to serious consequences for failure to achieve good recognition performance.

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