A radiometric reconstruction method for high-resolution remote sensing images

Abstract

The invention belongs to the field of image processing, and proposes a radiation reconstruction method for high-resolution remote sensing images, comprising the following steps: obtaining an area to be reconstructed t 1 The high-resolution remote sensing image L at the moment 1 ,as well as t 1 , t 2 The low-resolution remote sensing image M at time 1 , M 2 ; respectively for L 1 and M 1 , M 2 Perform band-by-band discrete wavelet transform processing; calculate the high-frequency component L of L2 2 ( b i ,HH), low frequency component L 2 ( b i ,HH), the mixed component of high and low frequency L 2 ( b i ,HL) and L 2 ( b i ,LH); carry out the inverse transform of the wavelet transform, and reconstruct t 2 The moment size is n x n The high-resolution remote sensing image L 2 . The invention more accurately describes, expresses and processes the time, space (especially texture features) and spectral feature information of multi-source remote sensing images, and can be widely used in the field of remote sensing image processing.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a radiation reconstruction method for high-resolution remote sensing images. Background technique [0002] The current radiometric reconstruction method of high spatial resolution remote sensing images is generally obtained by using reflectivity-based spatio-temporal fusion technology. The established radiation correlation model is applied to the high spatial-low temporal resolution remote sensing image of the corresponding time phase, and then the high spatial-low temporal resolution image of the known temporal phase and the low spatial-high temporal resolution data of the multi-temporal phase are used to predict Obtain the high space-low time resolution remote sensing image under the target phase. In the above data prediction process, the texture features and spectral features of ground objects are two key elements to obtain high-quality fusion results. At present, ...

AI Technical Summary

Problems solved by technology

In the above data prediction process, the texture features and spectral features of ground objects are two key elements to obtain high-quality fusion results. At present, some fusion methods that can effectively synthesize spectral features have been developed (such as STARFM (The Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm), but in view of the spatial scale difference between high-spatial resolution images and low-spatial-resolution images, the description of the fusion process for texture features is not yet mature, generally relying on multi-time series (two or more) high-level Spatial resolution data (such as ESTARFM (Enhanced STARFM) algorithm) or low processing efficiency (such as spatio-temporal fusion algorithm based on sparse learning)

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