Remote sensing image fusion method of adaptive simplified pulse coupling neural network model based on multi-scale morphological gradient

Abstract

The invention belongs to the field of remote sensing image fusion, and particularly relates to a remote sensing image fusion method based on a multi-scale morphological gradient self-adaptive simplified pulse coupling neural network model. The method comprises the following steps: acquiring a synthetic aperture radar image and a multispectral image; gaussian curvature filtering decomposition and Gaussian filtering decomposition are carried out on the synthetic aperture radar image to obtain a small-scale image, a large-scale image and a base-scale image; performing color saturation intensity conversion on the multispectral image to obtain an intensity image; fusing the base scale image and the intensity image to obtain an approximate image; fusing the approximate image, the small-scale image and the large-scale image to obtain a fused intensity image; reconstructing the fused intensity image to obtain a fused remote sensing image; according to the method, the scale morphological gradient and the adaptive simplified pulse coupling neural network are combined, and the phenomena of a block effect and local image blurring are avoided.

Description

technical field [0001] The invention belongs to the field of remote sensing image fusion, in particular to a remote sensing image fusion method based on an adaptive simplified pulse-coupled neural network model based on multi-scale morphological gradients. Background technique [0002] With the continuous development of remote sensing application technology, the requirements for the quality of remote sensing images in the fields of environmental protection, weather forecasting, military investigation, smart agriculture, and marine applications continue to increase. The remote sensing images of a single sensor can no longer meet the current application requirements. Remote sensing image fusion is to synthesize single-modal images acquired by multi-source sensors to generate multi-modal images, remove redundant information, add complementary information, make up for the lack of information features of single-modal images, and improve the completeness of information details of f...

AI Technical Summary

Problems solved by technology

This type of algorithm can achieve effective fusion in the spectral domain, but the spatial resolution of the fused image is low

Images