Multi-Method Panchromatic and Multispectral Image Fusion

Panchromatic and multispectral image fusion is a key technology in remote sensing image processing, aiming to combine high-resolution panchromatic images with low-resolution multispectral images to produce fused images that possess both high spatial resolution and rich spectral information. Below are several common fusion methods and their implementation approaches:

IHS Transformation (Intensity-Hue-Saturation) The IHS transformation is based on color space conversion, transforming multispectral images from RGB space to IHS space. The high-frequency information from the panchromatic image replaces the I component (intensity), followed by inverse transformation back to RGB space. This enhances spatial resolution while preserving multispectral information. Implementation typically involves color space conversion functions and matrix operations to handle component replacement.

High-Pass Filtering (HPF) The HPF method extracts high-frequency details from the panchromatic image and directly superimposes them onto the low-frequency components of the multispectral image. This method is simple and efficient but may introduce spectral distortion, particularly in regions with strong high-frequency information. Code implementation often uses convolution operations with high-pass filters (e.g., Laplacian or Gaussian difference filters).

GIHS Method (Generalized IHS) GIHS is an improvement over traditional IHS, reducing spectral distortion through weight adjustments or more complex transformation matrices. It is more suitable for fusing images from different sensors or band ranges, offering greater adaptability. Algorithm implementation involves modifying transformation matrices and incorporating adaptive weighting mechanisms.

Wavelet Transform Wavelet transform effectively separates high and low-frequency components of images. During fusion, the low-frequency components of multispectral images are combined with the high-frequency components from wavelet-decomposed panchromatic images. This improves resolution while better preserving spectral characteristics. Implementation requires wavelet decomposition functions (e.g., using Daubechies wavelets) and coefficient fusion rules.

PCA (Principal Component Analysis) PCA extracts primary information from multispectral images through principal component analysis, replacing the first principal component (PC1) with the panchromatic image before inverse transforming back to the original space. This method enhances spatial details while reducing dimensionality but may lose some spectral information. Code implementation involves covariance matrix calculation, eigenvalue decomposition, and component substitution.

Brovey Transform Brovey transform is a fusion method based on color normalization, enhancing visual contrast through ratio operations between multispectral bands and the panchromatic image followed by reweighted combination. It is computationally simple but prone to distortion under non-uniform lighting conditions. Implementation typically involves per-pixel normalization and band-weighted summation operations.

Each fusion method has its advantages and limitations. Selecting an appropriate method requires comprehensive consideration of application scenarios, computational efficiency, and requirements for spectral and spatial information preservation.