Assessment of Atmospheric Correction Methods for Hyperspectral Remote Sensing Imagery Using Geospatial Techniques

Abstract

Atmospheric correction is a main problem in visible or near-infrared remote sensing images since the existence of the atmosphere continuously influences the radiation from the ground to the sensor. Hence, atmospheric correction is necessary. Remotely sensed imagery has noise affected by atmospheric particles that can unclear the image and make quantitative analysis unreliable. The aim of this research is to evaluate atmospheric correction methods for remotely sensed imagery using ENVI software to get accurate results. In this research, three methods of atmospheric correction have been selected depend on the essential parameters whether from the image or field for hyperspectral imagery by using ENVI software. These methods include: IAR Reflectance, Flat field correction, and Empirical Line Correction. The results showed the corrected images from three methods give better interpretation of Z-profile than original image which helps to determine the absorption feature and increase the possibility to get a good result after processing. IAR Reflectance method gives easier spectral curve to be interpreted when comparison between radiance spectrum and reflectance spectra than other correction methods.

Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques

“…Pre Processing of Satellite Images. In general, pure satellite images contain inevitable mistakes and will not be explicitly used to identify features and applications [7,9]. It requires specific rectification.…”

“…Until the primary data analysis and data retrieval, pre-processing is completed. Two significant procedures include pre-processing, Geometric correction, and cloud correction or radiometric correction [9]. ERDAS 14 software [10] was used in this paper to perform most pre-processing phases of a satellite image.…”

Non-Binary Snow Index for Multi-Component Surfaces

“…On the other hand, the Sentinel-2A L1C data were pre-processed using the Sen2Cor algorithm on the SNAP to produce Bottom-Of-Atmosphere (BOA) reflectance data. According to Merzah et al [42], the Internal Average Relative Reflectance (IARR) correction technique removes or reduces the effects of the atmosphere providing a clean spectral curve to be interpreted. In this research, the IAR reflectance correction was applied to the Landsat and Sentinel-2A datasets to reduce the atmosphere effects and obtain similar atmospheric conditions [43][44][45].…”