HYPERSPECTRAL


HYPERSPECTRAL REMOTE SENSING FOR WILD OLIVE TREE DETECTION AND MAPPING


Hyperspectral sensor for Air Borne to be used for mapping wild olive tree.

The hyperspectral camera acquires the light intensity (radiance) for a large number (typically a few tens to several hundred) of contiguous spectral bands. Every pixel in the image thus contains a continuous spectrum (in radiance or reflectance) and can be used to characterize the objects in the scene with great precision and detail.

The hyperspectral camera is capable of acquiring data well beyond the spectral range of the human eye, which is limited by a maximum wavelength of about 700nm. The cameras can be configured for imaging out to 2500nm, thus including a large portion of the infrared spectrum. For many applications, the reflection/absorption properties in the IR region are essential to characterize, quantify or classify the objects in the scene.

Following the recent advances in sensor development and computing power, hyperspectral imaging is now ready to take the step from slow and unreliable research prototypes to reliable and accurate analytical instruments for applications ranging from online industrial monitoring/sorting/classification to laboratory measurements, clinical instruments for medical diagnostic and airborne and satellite based remote sensing tools.

Hyperspectral imagery provides opportunities to extract more detailed information than is possible using traditional multispectral data. The availability of commercial hyperspectral analysis tools is good, and these tools are continually becoming easier to use and more effective. Many airborne hyperspectral sensors are currently operating, and at least one spaceborne hyperspectral sensor is providing imagery for the general public. The future of hyperspectral remote sensing is promising. As newly commissioned hyperspectral sensors provide more imagery alternatives, and newly developed image processing algorithms provide more analytical tools, hyperspectral remote sensing is positioned to become one of the core technologies for geospatial research, exploration, and monitoring.

Many hyperspectral analysis approaches require the use of known material spectra. Known spectra can guide spectral classifications or define targets to use in spectral image analysis. Some investigators collect spectral libraries for materials in their field sites as part of every project. Several high quality spectral libraries are also publicly available. Some investigators derive spectral libraries from the image to be analyzed using specially designed algorithms available in commercial software. This approach ensures that the spectra will always be exactly comparable to the image pixel spectra.