Remote sensing image batch toning method based on l alpha beta space color mapping

Abstract

The invention provides a remote sensing image batch toning method based on l alpha beta space color mapping, which comprises the following steps: S1, carrying out image downsampling on a high-resolution image to obtain a low-resolution image; s2, performing coarse classification on the images according to the ground feature type composition involved in the images, combining adjacent images with similar ground feature type composition as image blocks, and performing partition toning by taking the image blocks as units; s3, selecting a partial region image in each partition as a feature image, and toning the feature image; s4, taking the feature image as a reference image, realizing color transfer based on an FCM clustering algorithm, and toning other images of each partition; and S5, performing color mapping on the original high-resolution image by using the low-resolution image toning result. According to the method, color mapping from the down-sampling image to the high-resolution image is realized through histogram matching of the l alpha beta space, so that the high-resolution remote sensing image meeting the visual effect is quickly obtained.

Description

technical field [0001] The invention belongs to the technical field of batch color adjustment of remote sensing images, and in particular relates to a method for batch color adjustment of remote sensing images based on lαβ space color mapping. Background technique [0002] As a technical means that can collect surface space information and dynamic change information in a macroscopic and high-time manner, remote sensing has been used in various fields such as meteorology, agriculture, environmental monitoring, disaster analysis, land use, and global change from traditional surveying and mapping and military aspects. direction. However, due to weather conditions, human factors, and sensor factors, the acquired remote sensing images often have problems such as color distortion and inconsistent brightness, which make them unable to be directly applied to various fields. Remote sensing image color matching technology can solve the phenomenon of color and brightness deviation in ...

AI Technical Summary

Problems solved by technology

Most of the traditional color correction methods are based on the original RGB image. On the one hand, with the continuous improvement of sensor resolution, the amount of image data has increased geometrically, and the requirements for algorithm performance and manual intervention have increased significantly. How to improve image color correction? Work efficiency has become a bottleneck problem restricting the application of large-scale high-precision remote sensing images; on the other hand, due to the high correlation between the three channels of the traditional RGB space, if you want to change the pixel color smoothly and naturally, you must combine the values ​​of the three channels. Modify accordingly, leading to increased complexity in the color adjustment process

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