Color equalization method of coastal area based on ocean vector file and image channel threshold

Abstract

The present invention discloses a method for color uniformity in the seaside area based on ocean vector files and image channel thresholds. S2. Perform pixel value aggregation and statistics on n different optical channels of the continental template image near P to obtain the continental template image near P. The n different optical channels correspond to n pixel range pixs(c 1 , c 2 ,...,c n ). The invention not only combines the vector file of geographic information (GIS) and the raster file of remote sensing (RS) image, breaks the barriers of subdivided industries, and solves the problem of low recognition rate of a single method and time-consuming and labor-intensive problems. The accuracy rate of land and sea recognition is improved, and the dynamic threshold method is adopted to improve the recognition rate of the traditional fixed threshold method. Post-processing by hand.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a seaside area color equalization method based on an ocean vector file and an image channel threshold. Background technique [0002] In the current technology for leveling seaside images, the main purpose of the design of the land optical satellite is to shoot land images, and the seawater is very different from the land resources due to various physical and chemical characteristics, resulting in the seawater captured by the land optical satellite. The image will be quite different from the terrestrial image (including inland rivers and lakes) in various parameters; [0003] Ocean vector map is a method of marking conventional ocean boundary conditions, which is related to the time and scale of the background data referenced when the vector map itself is made. There are differences in the reference backgrounds of the vector graphics at that time, and dire...

AI Technical Summary

Problems solved by technology

[0005] At the same time, the threshold method and the sea-land boundary detection method all have low recognition rates. It is extremely time-consuming and manpower-intensive to use manual image processing for coastal areas, and for ocean recognition in coastal areas based on neural networks. A large amount of training data is required, and this part of the training data requires a lot of manpower to label, which is very time-consuming, and requires a lot of manpower and material resources to have training data, while training the model on the training data requires a lot of computing resources, and the effect will be the same as The training data is strongly correlated, and the universality is poor

And assuming that after the model training is completed, the recognition of the image needs to be calculated by the neural network, which leads to the recognition will be very slow, and the usage scenarios are limited.

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