Short-wave infrared (SWIR) spectrum is invisible to the human eye and also to mainstream CCD and CMOS based imagers. This non visible spectrum on other hand caries other very useful features such as molecular vibrational mode information, which makes this wavelength range a target for molecular spectroscopy applications. Spectroscopy is a powerful non-contact technique for quickly recognizing and characterizing different materials/liquids/gasses and their features through the variations in absorption or emission of different electromagnetic waves. Different molecules act differently at different spectrum wavelengths which opens a field of various applications in different areas.
Nowadays SWIR cameras found it’s usefulness in many different industries beside satellite remote sensing applications also in pharmaceuticals (quality/counterfeit control), biomedical (deep tissue in vivo monitoring), agriculture (food/drug/soil/plant inspection), environment (water and air quality), petrochemicals (oil and gas analysis), manufacturing (chemicals and plastics control), security (surveillance), recycling (sorting), geology (mineral inspection) or semiconductor industry.
SkyLabs mastered SWIR technology in next application areas:
Automated textile substrate identification.
Home appliances (washing machine, washer drier, laundry ironing machine etc.) need to know the textile substrate composition of the textile products or cloths in order to properly set the parameters of the washing/drying/ironing (laundry) process. The most important parameters of the laundry processes which depend on the textile substrate classification output are the operational temperature, bath ratio, centrifuge speed, and laundry rubbing intensity. Using inappropriate laundry process parameters for a certain textile substrate can lead to a serious damage to the processed garment. Automatically predicting appropriate program provide users an added value, increasing living standard.
Industry laundry machine service providers can automatically recognize laundry type and use appropriate washing program.
Cloth manufacturers or purchase departments can with the non-invasive textile substrate identification verify the quality, composition, and declaration correctness of the input raw materials used during garment manufacture.
Soil quality addressing smart farming.
Agricultural sector is one of the main producers of greenhouse gases (GHG). As mentioned in Paris Agreement low greenhouse gas emissions development, in a manner that does not threaten food production, reducing influence on climate change and preserving sustainable lifestyles is global challenge. IoT enabled in-situ sensory system, capable of simultaneously capturing GHG emissions together with temperature and moisture of soil and air as well as levels of plant photosynthesis with geo-location data fused with weather and climate forecast, and other EO data products by the principles of deep- and feature-learning opens new possibilities for better understanding of the Earth system.