Challenges of Hyperspectral Imagery [HSI]

While hyperspectral imaging offers many advantages, high spatial resolution is not one of them when compared to many sub-meter imaging systems. However, a high spectral resolution offers unique insights, and most of the time, it’s sufficient to distinguish objects effectively smaller than the pixel dimensions.

Still, it is important to understand the challenges faced through hyperspectral imagery collection and processing.

1. Data dimensionality: Due to the large number of spectral bands entering the sensor aperture, there may be limitations when splitting the continuous spectrum and recording the bands. This leads to complexity and diversity in the high-dimensional characteristics of the hypercube, which may cause challenges in data analysis, interpretation, and classification.
   
   
2. Band determination and selection: Not all band combinations serve a purpose. Determining application-specific bands enables the optimal adaptation of hyperspectral data for specific requirements. This optimizes sensor resources, reduces noise, enhances classification accuracy, and facilitates interpretation and data interoperability.
   
   
3. Image processing tools: Techniques for processing hyperspectral imagery are unique and often non-parametric. Furthermore, image processing techniques for multispectral imagery are not adequate for understanding hyperspectral imagery. Processing hyperspectral imagery involves computationally intensive and complex algorithms both for the pre-processing of raw images and post-processing, such as image segmentation, classification, analysis, etc. Developing algorithms that balance accuracy and speed is a challenge for applications where real-time processing is crucial (e.g., disaster response and precision agriculture).
   
   
4. Signal to Noise Ratio (SNR): Maintaining consistent data quality becomes a challenge due to variability in the SNR. Low SNRs in specific bands make it difficult to accurately capture and interpret information across specific wavelength ranges. Moreover, atmospheric interferences such as water vapour, aerosols, and gases can affect the observed spectral signatures, reducing the SNR. Instrumental noise from the sensor (e.g., electronic components, thermal fluctuations, and sensor calibration issues) can deteriorate the SNR and the resultant image quality.
   
   
5. Data handling, Storage, and Transmission: Large volumes of data with equally voluminous information pose challenges when storing and transmitting hyperspectral data, making it resource-intensive. Data management and archiving become difficult in such cases.
   
   
6. Integration with other remote sensing data: Differences in the spatial and temporal resolution of hyperspectral imagery with respect to other datasets (such as LiDAR or RADAR) demand the development of effective fusion techniques, essential for obtaining comprehensive information. Ensuring consistency in the number and type of bands and optimal SNRs facilitates effective data fusion and interpretation.

To address these challenges, Pixxel has employed very unique sensor technology and processing techniques designed through decades of aggregated experience of our spectral imaging scientists. Patented calibration and correction techniques are employed to optimize the data without compromising critical spectral and radiometric integrity.