MATLAB Implementation for Calculating Image Information Entropy in Remote Sensing Fusion

In this experiment, we employed MATLAB to compute the information entropy of fused remote sensing images, specifically focusing on IHS and PCA fusion techniques applied to TM and SPOT images. The implementation involves several key steps: first, we decomposed the images into different spectral bands and performed preprocessing operations to enhance computational efficiency and accuracy. We then applied Shannon's entropy formula to calculate the entropy value for each individual band, followed by a weighted averaging process to derive the overall information entropy of the complete image. From a coding perspective, the implementation typically utilizes MATLAB's Image Processing Toolbox functions for image reading (imread) and conversion to appropriate formats. The entropy calculation involves creating probability distributions for pixel values using histogram functions (imhist) and applying the mathematical formula: H = -Σ(p(i) * log2(p(i))) where p(i) represents the probability of occurrence for each gray level. The weighted averaging across bands is implemented through matrix operations and normalization procedures. Beyond entropy calculation, MATLAB offers comprehensive capabilities for various image processing and analysis tasks including image enhancement, segmentation, and classification. These functionalities are particularly valuable in remote sensing applications, enabling researchers to better understand and utilize remote sensing data. Mastering MATLAB's image processing toolkit provides significant advantages for remote sensing research and practical applications, offering numerous conveniences and benefits for data analysis workflows.