HyperEvalSR is an open-source project that provides evaluation metrics for hyperspectral image quality assessment. The included metrics are as follows:
- Peak Signal to Noise Ratio (PSNR): Measures the ratio between the maximum possible power of a signal and the power of corrupting noise.
- Reconstruction Signal-to-Noise Ratio (RSNR): Evaluates the signal-to-noise ratio of the reconstructed image.
- Degree of Distortion (DD): Represents the level of distortion in the image.
- Spectral Angle Mapper (SAM): Measures the spectral similarity between two images using the angle between their spectral vectors.
- Root Mean Squared Error (RMSE): Computes the square root of the average squared differences between the reference and reconstructed images.
- Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS): Calculates the relative global dimensionless synthesis error.
- Structural Similarity Index (SSIM): Assesses the structural similarity between the reference and reconstructed images.
In this context, we assume that the reference image and the reconstructed image obtained from the algorithm are denoted as
Peak Signal-to-Noise Ratio (PSNR) is commonly used to measure the similarity between a reconstructed image and an original image. It is expressed in decibels (dB), and a higher value indicates a higher similarity between the reconstructed and original images. The calculation formula for PSNR is as follows:
where
In this formula,
The RMSE is a commonly used indicator to describe the degree of difference between the reconstructed image and the reference image. Smaller errors result in smaller RMSE values. When the reconstructed image and the reference image are exactly the same, the RMSE equals 0. The RMSE is defined as:
The definition of
The RSNR is commonly used to measure the spatial quality of the reconstructed image. Higher RSNR values indicate smaller differences between the reconstructed and original images, and thus better image quality. The RSNR is calculated as:
$$ \mathrm{RSNR}=10\log {10}\left( \frac{||\mathbf{X}||{F}^{2}}{||\widehat{\mathbf{X}}-\mathbf{X}||_{F}^{2}} \right)\tag{4} $$
The Degree of Distortion (DD) is an indicator used to describe the degree of signal distortion, typically used to evaluate the distortion during signal transmission or storage. Smaller distortions result in smaller DD values, with the optimal value being 0. The DD is defined as:
In this formula,
The Spectral Angle Mapper (SAM) compares the similarity between the reconstructed and reference images by measuring the spectral angle of each pixel. The higher the similarity, the smaller the SAM value. The SAM is calculated as:
$$ \mathrm{SAM}=\frac{1}{M} \sum_{n=1}^{M} \text{arccos} (\frac{(\widehat{\mathbf{x}}[n])^{\mathrm{T}} \mathbf{x}[n]}{|\widehat{\mathbf{x}}[n]|{2} \cdot | \mathbf{x}[n]|{2}})\tag{6} $$
In this formula,
ERGAS is a relative error indicator that can be used to compare the quality of reconstructed remote sensing images with different resolutions and sizes, as well as to evaluate image quality at different compression ratios. Smaller ERGAS values indicate higher spatial and spectral similarity between the reconstructed and reference images. The ERGAS is calculated as:
In this formula,
The Structural Similarity Index (SSIM) is an indicator used to evaluate the similarity between two images and to quantitatively assess the degree of image distortion. The SSIM value ranges between
In this formula,