Remote Sensing Image Fusion Method Based on Sparse Tensor Nearest Neighbor Embedding

Abstract

The invention discloses a sparse tensor neighborhood embedding-based remote sensing image fusion method. According to the technical scheme of the invention, the method comprises the following steps of 1), inputting a low-resolution and multi-spectral image M and a high-resolution and full-color image P; 2) dividing the low-resolution and multi-spectral image M into multi-spectral image tensor blocks M1; 3) constructing a high-resolution and multi-mode dictionary HD and a low-resolution and multi-mode dictionary LD based on the high-resolution and full-color image P and the multi-spectral image tensor blocks M1; 4) optimizing the atoms of the high-resolution and multi-mode dictionary HD and the atoms of the low-resolution and multi-mode dictionary LD; 5) solving the tensor sparse coefficient A of the multi-spectral image tensor blocks M1 in the low-resolution and multi-mode dictionary LD; 6) multiplying the tensor sparse coefficient A by the high-resolution and multi-mode dictionary HD, and obtaining a high-resolution and multi-spectral image after the airspace residual compensation. In this way, the information within multi-spectral bands is utilized, and the color distortion of a fused image is reduced. Therefore, the method can be used for remote sensing and target identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a remote sensing image fusion method, which can be used for remote sensing detection, safety navigation, medical image analysis, anti-terrorism inspection and environmental protection. Background technique [0002] Remote sensing image fusion is the process of processing image data and other information obtained by multiple remote sensors. One of the most widely studied is the fusion of multispectral images and panchromatic images. Multi-spectral images have rich color information but low spatial resolution, generally four bands. Panchromatic images have high spatial resolution and clear details, but lack color information. Generally, they use a single band from 0.5 μm to 0.75 μm. In target recognition, both color information and target spatial resolution play a very important role in distinguishing ground features. [0003] Remote sensing image fusion is the process ...

AI Technical Summary

Problems solved by technology

[0004] The fusion algorithm based on the model Model_Based still has deficiencies in maintaining spectral information: 1) ignoring the connection between bands leads to spectral distortion of the fusion results; 2) ignoring the application of high-order statistical properties

Because the algorithm fuses the multispectral image into different bands, it fails to fully consider the full use of the information between the bands, resulting in color distortion and spectral distortion in the fusion results.

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