Hyperspectral image interested area automatic extraction method based on active contour model

Abstract

The present invention discloses a hyperspectral image interested area automatic extraction method based on an active contour model. The method comprises the steps of firstly establishing a spectral reflectivity standard hybrid vector of an interested ground object according to a spectral reflectivity vector of a known pure ground object pixel; then calculating a correlation coefficient of the spectral reflectivity standard hybrid vector of the interested ground object and a spectral vector of each pixel in a to-be-processed hyperspectral image to obtain a pixel correlation coefficient deviation matrix; finally, constructing a C-V active contour model by the pixel correlation coefficient deviation matrix, and further utilizing a finite difference method to solve the model to thereby extract the pixels of an interested area. A test result of the embodiment shows that the method of the present invention can obtain an extraction result obviously better than that of a conventional C-V model with less iteration times.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an active contour model-based automatic region-of-interest extraction method for a hyperspectral image that can distinguish object types and has a fast calculation speed. Background technique [0002] At present, hyperspectral remote sensing technology is developing towards higher spatial resolution, higher spectral resolution and higher temporal resolution, which makes the data volume of hyperspectral images increase exponentially. Take the AVIRIS (AirborneVisible / Infraed Imaging Spectrometer) type hyperspectral image as an example, it has 224 continuous bands, each band image contains 512×614 pixels, each pixel occupies 16bit, and its storage space exceeds 140M byte. Therefore, high-efficiency coding of hyperspectral remote sensing images is one of the methods to alleviate the contradiction between information acquisition and transmission of hyperspectral image data. [0003] ...

AI Technical Summary

Problems solved by technology

[0004] Existing methods for extracting regions of interest from hyperspectral images mostly use typical methods such as the maximum displacement method, some important bit-plane displacement methods, and one-by-one bit-plane displacement methods, but these methods do not consider the geological features of hyperspectral images. (such as vegetation, water bodies, rock mines, soil, urban artificial targets, etc.), it is possible to divide pixels belonging to different ground objects into the same area of ​​interest, and the calculation speed is slow, which affects the further practical application of hyperspectral images

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