High-resolution remote sensing image cloud snow identification method and device fusing topographic data and deep neural network

Abstract

The invention discloses a high-resolution remote sensing image cloud snow identification method and device fusing topographic data and a deep neural network, and the method comprises the following steps: S1, inputting a high-resolution remote sensing image into a DeepLabv3 + semantic segmentation neural network model, and inputting the topographic data of the corresponding position of the remote sensing image into a topographic feature extraction network model; S2, outputting the topographic features extracted by the topographic feature extraction network model to a DeepLab v3 + semantic segmentation neural network model, and fusing the topographic features with deep features in the DeepLab v3 + semantic segmentation neural network model; and S3, enaling the DeepLab v3 + semantic segmentation neural network model to output cloud pixels and snow pixels in the high-resolution remote sensing image. The topographic features are fused through the topographic feature extraction network, and the channel attention module is introduced into the DeepLab v3 + semantic segmentation neural network model, so that in the application of mountainous area cloud snow identification, the model cloud snow identification precision can be improved, the condition of mutual wrong classification of cloud snow can be reduced, and meanwhile, the model prediction time is shortened.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and more specifically, the invention relates to a high-resolution remote sensing image cloud and snow recognition method and device that integrate topographic data and deep neural network. Background technique [0002] As an important part of the cryosphere, snow cover is one of the most active natural elements on the earth's surface, and plays an important role in climate change research and water resource utilization in arid and semi-arid regions. On the one hand, snow cover has a high albedo, its accumulation and melting are always accompanied by energy budget, and it also has a sensitive feedback effect on climate change, playing an extremely important role in the global or regional climate system. On the other hand, seasonal snow accumulation is one of the main sources of freshwater resources in arid and semi-arid areas of my country. Snowmelt runoff in spring accounts for more tha...

AI Technical Summary

Problems solved by technology

Therefore, it is reliable to identify snow cover in mountainous areas combined with terrain data, but at present, the deep neural network cloud and snow identification based on high-resolution remote sensing images does not effectively incorporate terrain data.

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