A method for extracting green tide information from remote sensing images based on deep learning and super-resolution

Abstract

The invention belongs to the technical field of remote sensing image processing, and discloses a method for extracting green tide information from remote sensing images based on deep learning and super-resolution. The model is pre-trained, and a pre-trained super-resolution network model is obtained; II. A semantic segmentation network is constructed; III. A green tide extraction model is obtained based on the pre-trained super-resolution network model and the semantic segmentation network, and the green tide is trained. Extraction model; IV. Obtain the MODIS image of the green tide information to be extracted and input the trained green tide extraction model to obtain the corresponding green tide extraction result. The method of the invention can replace the traditional artificial threshold method; by integrating the image super-resolution reconstruction technology into the semantic segmentation network, the final segmentation performance is improved on the premise of improving the image quality; due to less human factors, the green tide extraction result is accurate and stable .

Description

technical field [0001] The invention relates to a method for extracting green tide information from remote sensing images based on deep learning and super-resolution. Background technique [0002] Green tide is an algal bloom phenomenon formed by the explosive proliferation and aggregation of macroalgae (such as prolifera) in the ocean under specific environmental conditions. The large-scale outbreak of green tide will not only cause marine disasters, but also affect the landscape and interfere with the development of tourism. [0003] The green tide monitoring method based on traditional ship navigation consumes a lot of manpower and material resources, while satellite remote sensing technology can accurately and timely obtain information such as the location and distribution of green tide outbreaks, so it has the irreplaceable advantages of traditional methods. [0004] Moderate-Resolution Imaging Spectroradiometer (MODIS) is widely used in real-time monitoring of green t...

AI Technical Summary

Problems solved by technology

[0014] Although deep convolutional networks have achieved success in image recognition tasks, they are rarely used in remote sensing image green tide extraction and semantic segmentation tasks. The reason is that deep convolutional neural networks are applied to low-resolution remote sensing For the task of extracting green tide information from images, the network structure construction and training methods still need to be explored

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