Aerial image water body extraction method and system based on deep learning

Abstract

The invention provides an aerial photography water body extraction method and system based on deep learning. The method comprises the steps of: collecting A multispectral image, obtaining an RGB imagethrough true color synthesis, and obtaining a hue image and a gray level image through color space conversion; obtaining a water body characteristic graph F1 according to the water body characteristic index; obtaining an energy texture feature map and an entropy texture feature map of the grayscale image according to the grayscale co-occurrence matrix, and obtaining a fine-grained feature map F2of the water body in combination with the tone map; inputting the RGB image, the F1 and the F2 into a trained semantic extraction encoder to obtain a feature map F3; and calculating a plurality of color moments of the RGB image, inputting the color moments into the FC for full connection, outputting a plurality of neurons, multiplying the neurons by the corresponding feature maps, finally extracting water body features through a decoder, and outputting a water body segmentation map.

Description

technical field [0001] This application relates to the field of artificial intelligence, in particular to a method and system for extracting water from aerial images based on deep learning. Background technique [0002] With the continuous expansion of the application field of UAVs, it has become an important application method to use UAVs to extract ground objects from the ground surface. We can not only distinguish ground objects according to the difference in shape and structure of multispectral remote sensing images, but also distinguish ground objects according to their spectral characteristics, which provides the possibility for computer recognition and classification of ground object images. At present, for the water body extraction of multispectral images, there are problems of weak representation ability, generalization ability, and extremely unbalanced sample categories, and it is more difficult to fine-grained segmentation of water bodies, which usually shows that...

AI Technical Summary

Problems solved by technology

At present, for the water body extraction of multispectral images, there are problems of weak representation ability, generalization ability, and extremely unbalanced sample categories, and it is more difficult to fine-grained segmentation of water bodies, which usually shows that there are a large number of segmentations in ponds, rivers, lakes and other categories. False, difficult to distinguish between different bodies of water

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