Working of HSI Images
These are the general steps we need to follow:
- Acquiring Raw Data Cubes: Start with the raw hyperspectral data cubes, which contain information about each pixel’s spectral properties across different wavelengths.
- Data Preparation and Quality Assessment: Clean up the data cubes by removing any unwanted noise or distortions that may affect the accuracy of our analysis. This ensures that the data is reliable and ready for further examination. Assess the quality of the images to ensure they meet the necessary standards for analysis. This involves checking factors like resolution, clarity, and overall image fidelity.
- Smoothing Spectral Data: Smooth out the spectral data using Savitzky-Golay filtering techniques. This helps to reduce noise and enhance the clarity of spectral features, making it easier to identify patterns and anomalies in the data.
- Assigning Labels and Categories: Assign labels or categories to specific areas of interest within the data cubes. This step provides context to the analysis by identifying regions with distinct spectral signatures, such as different land cover types or material compositions.
- Selecting Relevant Spectral Bands: Choose the most relevant spectral bands from the data cubes based on the analysis objectives. By selecting the appropriate bands, we can focus on specific features or properties of interest while reducing the complexity of the data.
- Performing Spectral Classification: Use classification techniques to categorize pixels within the hyperspectral data cubes based on their spectral signatures. This can involve machine learning algorithms or spectral libraries to classify pixels into different classes or categories, such as land cover types or material compositions.
This is how we analyze Hyperspectral image data.
What is Hyperspectral Imaging? Where it is used?
Spectral imaging integrates two distinct fields: spectroscopy and photography, to capture image data across numerous wavelength bands. Typically, spectral imaging is categorized into multispectral, which involves sampling fewer than 20 wavelength bands, and hyperspectral, which encompasses sampling more than 20 wavelength bands.
In this article, we are going to discuss What Hyperspectral Imaging means and where it is used in detail.
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