Applications of remote sensing using machine learning:
Remote sensing of water:
As water is very precious and important in the earths ecosystem it is important to study about the water. With the use of remotely sensing methods oceanographer, other environmental and marine researchers studying all the effects through examining the big areas. Images clicked by the satellites of NASA are used to remote sense the ocean to find out the chlorophyll levels and surface temperature, these images are also useful for the costal mapping and lake remote sensing.
Natural water body absorption and transmission properties can understand clearly with high spectral resolution of water bodies. Research can get the values of the water-leaving radiance by examining the water radiation without any measurements. This helps researchers to extend their study in developing new algorithms to analyze the images of the water body that are received from the satellites.
Remote sensing for geology:
Geoscience is classified into many subfields such as paleontology, geomorphology, structural geology etc. with these classification researchers also need to find the physical and chemical properties of the things found on earth’s surface.In the field of geology and the mineral exploration remote sensing is useful and beneficial method of giving access to harmful sites. AVIRIS, HyMap, ASTER are some multispectral and hyperspectral sensors from which we can get the aerial images which are used in many surveys and geomorphology applications. To offer various resolution levels, configurations Field spec spectroradiometer and Terraspec mineral analyzer to collect the field spectra.
Crops and soil analysis:
Scientists have used visible near infrared spectroscopy to know th...
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...ected from the phytoplankton reveals to light is associated with the volume of light observed by phytoplankton.
This paper summarizes about the machine learning fields in remote sensing. It not only covers standard machine learning features but also gives the clear view about the new ways of remote sensing compare to the old ways. Explaining about how machine learning algorithms are applicable in remote sensing by taking ocean water as an example. Paper also reviews different data processing levels that are carried out in remote sensing which are very useful in real time image processing and many applications. The result of this study provided me the potential of classifications, performance and also different integration of algorithms into real time application. Even if it is of various varieties and non-linear systems behavior can be known successfully.
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