Arithmetic and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques

TL;DR

This paper compares arithmetic and frequency filtering pixel-based image fusion techniques in remote sensing, evaluating their effectiveness in enhancing spatial resolution while preserving spectral information using various quality metrics.

Contribution

It provides a comparative analysis of multiple pixel-based image fusion methods, including both arithmetic and frequency filtering techniques, with a focus on quality assessment metrics.

Findings

Frequency filtering methods generally outperform arithmetic methods in preserving spectral information.

Wavelet transform-based fusion shows significant improvement in image quality metrics.

Evaluation using multiple metrics offers comprehensive insights into fusion performance.

Abstract

In remote sensing, image fusion technique is a useful tool used to fuse high spatial resolution panchromatic images (PAN) with lower spatial resolution multispectral images (MS) to create a high spatial resolution multispectral of image fusion (F) while preserving the spectral information in the multispectral image (MS).There are many PAN sharpening techniques or Pixel-Based image fusion techniques that have been developed to try to enhance the spatial resolution and the spectral property preservation of the MS. This paper attempts to undertake the study of image fusion, by using two types of pixel-based image fusion techniques i.e. Arithmetic Combination and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques. The first type includes Brovey Transform (BT), Color Normalized Transformation (CN) and Multiplicative Method (MLT). The second type include High-Pass Filter Additive Method (HPFA), High-Frequency-Addition Method (HFA) High Frequency Modulation Method (HFM) and The Wavelet transform-based fusion method (WT). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including Standard Deviation (SD), Entropy(En), Correlation Coefficient (CC), Signal-to Noise Ratio (SNR), Normalization Root Mean Square Error (NRMSE) and Deviation Index (DI) to estimate the quality and degree of information improvement of a fused image quantitatively.