Open AccessCombined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD
Author(s) -
Fan Pei-li
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.100
Subject(s) - image fusion , artificial intelligence , computer science , feature (linguistics) , computer vision , feature detection (computer vision) , remote sensing , block (permutation group theory) , affine transformation , pattern recognition (psychology) , image (mathematics) , image processing , geography , mathematics , philosophy , linguistics , geometry , pure mathematics
For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.