Hyperspectral remote sensing image vector C-V model segmentation method based on wave band selection

Abstract

The invention discloses a hyperspectral remote sensing image vector C-V model segmentation method based on wave band selection. Firstly, according to a spectral curve, wave bands high in contrast ratio between a target and the background are selected, further, according to relevant coefficients of the wave bands, the wave bands high in relevancy are removed so that a new wave band combination can be formed, and therefore according to the determined wave band assembly, a hyperspectral image vector matrix is established; on the basis, a vector C-V segmentation model based on the vector matrix is constructed, the edge guiding function based on gradient is introduced into the model, on the basis that a traditional C-V model is reserved to perform image segmentation based on area information, the capacity for capturing the target boundary in heterogeneous areas and under complex background conditions is enhanced through edge detail information of images, segmentation precision of the hyperspectral remote sensing images is improved, segmentation speed of the hyperspectral remote sensing images is increased.

Description

technical field [0001] The invention belongs to a hyperspectral remote sensing image data segmentation method, in particular to a hyperspectral remote sensing image segmentation vector C-V model segmentation method based on band selection, which is suitable for fast and accurate segmentation of hyperspectral remote sensing images in heterogeneous regions and complex backgrounds . Background technique [0002] The rapid development of imaging spectroscopy has brought remote sensing technology into the stage of hyperspectral remote sensing. A hyperspectral image can be regarded as a three-dimensional image composed of a two-dimensional spatial dimension and a one-dimensional spectral dimension. Each two-dimensional image describes the spatial characteristics of the earth's surface, while the spectral dimension reveals the spectral curve characteristics of each pixel of the image. The characteristics of hyperspectral images are different from natural images. They have the char...

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Problems solved by technology

The characteristics of hyperspectral images are different from natural images. They have the characteristics of large amount of data, high spectral resolution, relatively low spatial resolution, complex and diverse shapes and structures, and rich types of ground objects. Therefore, hyperspectral remote sensing image segmentation It has the following problems: on the one hand, hyperspectral remote sensing images contain rich ground object information and also have a lot of redundancy. Directly using the spatial information of hundreds of bands for vector C-V model segmentation will cause a huge amount of calculation and affect the efficiency of the algorithm. On the other hand, there is no obvious edge between the object and the background of the hyperspectral remote sensing image, and it is difficult to achieve the ideal segmentation effect only by relying on the gradient information at the boundary between the object and the background; and it is also difficult to rely on the regional information in the image To achieve the ideal segmentation effect

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