Quels sont les principaux défis et opportunités de l’imagerie hyperspectrale pour l’exploration minérale?

HSI is similar to unveiling Earth's hidden treasures through the lens of hyperspectral eyes. Each natural material, like minerals, possesses a unique fingerprint that sets it apart from others. This fingerprint is a spectral signature, describing the interaction of minerals with light based on wavelength & amount of light reflected back to the sensor. Multispectral sensors offer a lighter structure of information, they can't retain many characteristics of the reflected light comparing to hyperspectral. Consequently, many features in mineral light reflectance remain unknown. Consider your own fingerprint with less lines on the finger skin-as results, your fingerprint becomes less unique, making it harder to find differences to others fingers

HSI can identify minerals based on their characteristic absorption and reflection features in the visible, near-infrared, and shortwave infrared regions of the spectrum. HSI can also identify and catalog spectral signatures for more than 4,000 naturally occurring minerals found on earth.

Challenges of HI for mineral exploration include the massive amounts of data generated, requiring significant computational power and advanced algorithms to process, as well as the high cost of hyperspectral sensors and data acquisition systems. Atmospheric effects and mixed pixels also complicate spectral analysis and mineral identification. However, hyperspectral imaging also offers opportunities for precise mineral identification, large-scale mapping, and improved exploration efficiency. Additionally, it can be used for environmental monitoring and remediation efforts, and can be integrated with other geophysical and geological datasets to provide a more comprehensive understanding of mineral deposits.

The physical factors mention here are also pertinent to multispectral. However, their impact is more pronounced in hyperspectral. Numerous papers & researches have focused on preprocessing & data cleaning techniques that prove beneficial for hyperspectral data. While some methods, such as Principal Component Analysis (PCA), are well-established & widely used across various data sectors, others, like Continuum Removal, are more specific to hyperspectral & spectroscopy. Eventually, domain knowledge is crucial for selecting the appropriate method that aligns with the specific goals of the task. For example, for the same group of minerals found in different locations globally, using the same preprocessing method may not yield consistent results

The main challenges of hyperspectral imaging (HSI) for mineral exploration include: Limited accessibility: HSI equipment can be expensive and inaccessible in remote areas. Data processing complexity: Analyzing large quantities of hyperspectral data requires expertise and advanced computing resources. Soil interference: HSI's sensitivity to surface materials can lead to interference from soil, vegetation, and weathering, posing challenges in differentiating mineral signatures. Tools like ENVI (Environment for Visualizing Images) and HySime (Hyperspectral Image Analysis Software) can aid in the analysis of hyperspectral data, simplifying the processing and interpretation of mineral signatures.

Hyperspectral imaging can map clays, talc, and other deleterious rock phases and produce valuable information for building predictive models of mining and geometallurgical parameters. For this purpose, and in addition to core logging, hyperspectral camera systems can be taken to the mining sites and installed on processing lines. The hyperspectral camera can produce data for rapid mineralogical mapping of the entire mine wall faces, whether onboard a ground vehicle or a drone.

How HSI can overcome the challenges: Advanced-Data Processing Tools: Software like ENVI and HySpex provides advanced algorithms for data interpretation. Geological Mapping: HSI can be utilized to create detailed geological maps for ground truth validation using tools like Specim IQ.

Hyperspectral imaging (HSI) can create opportunities for mineral exploration in the following ways: Detection of mineral signatures: HSI can identify unique spectral signatures of minerals, aiding in their detection and mapping. Target identification: It can pinpoint specific mineral deposits, enabling targeted exploration efforts. Remote sensing: HSI allows for non-invasive, large-scale exploration, reducing the need for costly and time-consuming fieldwork. Geological mapping: It can assist in creating detailed geological maps by identifying mineral assemblages and alterations. Exploration tool: HSI serves as a valuable tool alongside other exploration methods, enhancing the overall exploration process.

The advantages of reducing cost, time, and risk are not confined to Earth but extend to the exploration of other planets as well. Minerals essential to human use can be found on other celestial bodies, suggesting that the future of mineral extraction lies in space. Imagine discovering Aluminum deposits\clusters on different surfaces of planets through remote observation, eliminating the need to deploy rovers for in-situ tracking and measurement.

Challenges: Data Management: massive amounts of data can be challenging to manage, process, and analyze effectively. Noise and Interference: can degrade the quality of hyperspectral images and hinder accurate mineral identification. Ground Truth Validation: can be costly and time-consuming to acquire. Spatial Resolution: Achieving high spatial resolution can be challenging. Spectral Unmixing: essential for extracting mineral signatures, but complex and computationally intensive, requiring expertise in both remote sensing and mineralogy. Opportunities: Mineral Mapping Target Identification Remote Sensing Integration with Other Data Environmental Monitoring

Could wildfire management be another possibility? Would satellite orbits along with data processing times make this a practical tool.