Correction of Atmospheric Haze of IRS-1C LISS-III Multispectral Satellite Imagery: An Empirical and Semi-Empirical Based Approach

Abstract

The atmospheric effect greatly affects the quality of satellite data and mostly found in the polluted urban area in the great extent. In this paper, the atmospheric correction has been carried out on IRS-1C LISS-III multispectral satellite image for efficient results for the Raipur city, India. The atmospheric conditions during satellite data acquisition was very clear hence very clear relative scattering model of improved dark object subtraction method for the correction of atmospheric effects in the data has been carried out to produce the realistic results. The haze values (HV) for green band (band 2), red band (band 3), NIR band (band 4) and SWIR (band 5) are 79, 53, 54 and 124, respectively; were used for the corrections of haze effects using simple dark object subtraction method (SDOS). But the final predicted haze value (FPHV) for these bands are 79, 49.85, 21.31 and 0.13 that were used for the corrections of haze effects applying improved dark object subtraction method (IDOS). We found that IDOS method produces very realistic results when compared with SDOS method for urban land use mapping and change detection analysis. Consequently, ATCOR2 model provides better results when compared with SDOS and IDOS in the study.

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

“…LANDSAT-7ETM+ of month June, 2006 was downloaded on October 2, 2012, and it was distorted with scene line error and which was rectified using gap filling program in ENVI 4.8 software. Generally, the atmospheric effect is not corrected by data providers; it should be done by the users as a pre-processing task [29]. It was geometrically corrected using Survey of India (SOI) topographic maps (1:50,000, 63G/11 and 63G/12).…”

Integration of Earth Observation Data and Spatial Approach to Delineate and Manage Aeolian Sand-Affected Wasteland in Highly Productive Lands of Haryana, India

“…With the advent of satellite remote sensing mapping of degraded land has been started more efficiently at much finer scales [35]. The raw data which are affected by panoramic distortion, earth curvature, sensor detector failed, and detector line losses which are primarily corrected by data providers [36], hence, provide better mapping possibilities. Satellite remote sensing provides unbiased information about the objects.…”