Atmospheric correction of remote sensing imagery based on the surface spectrum’s vector space

Satellite images have a wide application for detection and identification of objects, phenomena and changes on the surface of the Earth. In this paper are presented methods for arable land identification based on Sentinel-2 satellite imagery and distribution of the obtained data. The images were atmospherically corrected, resulting in the true reflection values from the ground. The identification procedure is based on known annual crops land cover variation rules during seasons. The vegetation signal in the images was enhanced using the SAVI vegetation index, after which the detection of arable land was carried out by raster subtraction. As a result, data on the spatial distribution of 5 arable land classes were obtained: summer crops, winter crops, pastures, vineyards and orchards and unclassified land. In order to complete the final result, inhabited places and water surfaces were detected and mapped. The resulting data were vectorized and displayed through a three-layer GIS (Geographic information system) service-based architecture in accordance with OGC specification.

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Atmospheric correction of remote sensing imagery based on the surface spectrum’s vector space

Cited by 2 publications

References 11 publications

CPBAC: A quick atmospheric correction method using the topographic information

CPBAC: A quick atmospheric correction method using the topographic information

The use of remote sensing in arable land identification and mapping

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