AN ENHANCED ALGORITHM FOR AUTOMATIC RADIOMETRIC HARMONIZATION OF HIGH-RESOLUTION OPTICAL SATELLITE IMAGERY USING PSEUDOINVARIANT FEATURES AND LINEAR REGRESSION
The growing number of available optical remote sensing data providing large spatial and temporal coverage enables the coherent and gapless observation of the earth’s surface on the scale of whole countries or continents. To produce datasets of that size, individual satellite scenes have to be stit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1-W1/115/2017/isprs-archives-XLII-1-W1-115-2017.pdf |
| Summary: | The growing number of available optical remote sensing data providing large spatial and temporal coverage enables the coherent and
gapless observation of the earth’s surface on the scale of whole countries or continents. To produce datasets of that size, individual
satellite scenes have to be stitched together forming so-called mosaics. Here the problem arises that the different images feature
varying radiometric properties depending on the momentary acquisition conditions. The interpretation of optical remote sensing data
is to a great extent based on the analysis of the spectral composition of an observed surface reflection. Therefore the normalization
of all images included in a large image mosaic is necessary to ensure consistent results concerning the application of procedures to
the whole dataset. In this work an algorithm is described which enables the automated spectral harmonization of satellite images to a
reference scene. As the stable and satisfying functionality of the proposed algorithm was already put to operational use to process a
high number of SPOT-4/-5, IRS LISS-III and Landsat-5 scenes in the frame of the European Environment Agency's
Copernicus/GMES Initial Operations (GIO) High-Resolution Layer (HRL) mapping of the HRL Forest for 20 Western, Central and
(South)Eastern European countries, it is further evaluated on its reliability concerning the application to newer Sentinel-2
multispectral imaging products. The results show that the algorithm is comparably efficient for the processing of satellite image data
from sources other than the sensor configurations it was originally designed for. |
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| ISSN: | 1682-1750 2194-9034 |