Multi-feature multi-level visible light and high-spectrum image high-precision registering method

Abstract

The invention discloses a visible light image and high-spectrum image registering method which comprises the following steps: performing multi-scale decomposition on a visible light image to form a low-resolution visible light image; generating a high-spectrum image significant waveband image according to the high-spectrum image; extracting SIFT (scale invariant feature transform) features, multi-scale angular point features and surface point features from the low-resolution visible light image and high-spectrum image significant waveband image; matching the SIFT features and removing exterior points; obtaining a transformation model by use of the matched SIFT feature pair; extracting the multi-scale angular point features and surface point features based on an image block pair by taking a registered transformation model of the previous layer as an initial transformation model of the current layer on each layer of visible light image and high-spectrum image significant waveband image; selecting the transformation type and obtaining a transformation parameter according to the initial transformation and the multi-scale angular point features and surface point features in combination with an iteration re-weighted least square method; and transforming the high-spectrum image according to the transformation model to obtain the transformed high-spectrum image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a registration method for remote sensing images, in particular to a registration method for visible light and hyperspectral images. The invention can be widely applied to registration of remote sensing images acquired by aerospace and aviation sensor platforms. Background technique [0002] The hyperspectral image divides the spectral frequency band of the object into finer segments, and the continuous spectral curve reflects the material information of the target, which is of great significance for identifying camouflaged targets. For example, a real tank and a camouflaged rubber tank in the same environment cannot be distinguished on the visible light image; If the tank is placed in a heating object, it is still impossible to distinguish the authenticity of the tank; and the spectral curve of the hyperspectral image can correct its material, thereby accurately disting...

AI Technical Summary

Problems solved by technology

At present, most of the research on the registration of visible light and hyperspectral images follows the technical route of visible light image registration; however, due to the huge difference in spatial resolution and the poor quality of hyperspectral images (noise, leakage, distortion) and other factors, At present, there is no general, automatic and practical visible light-hyperspectral image registration algorithm

In practical applications, it is often time-consuming and labor-intensive to calibrate the control point pairs manually; moreover, when the spatial resolution differs by 40 times, the error of manual calibration is very large, and the registration accuracy cannot be guaranteed

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