Atmospheric Correction Of Hyperspectral Data Research Articles

Dark-object subtraction atmosphere correction for water body information extraction in Zhuhai-1 hyperspectral imagery

The atmospheric correction of hyperspectral data stands as a fundamental step in quantitative applications, crucial for the accurate analysis of hyperspectral information. Zhuhai-1 hyperspectral data, characterized by its high spatial and spectral resolution, holds substantial potential and advantages for the quantification of water body information. Nonetheless, the adoption of more precise physical models for atmospheric correction often demands extensive satellite and ground environmental parameters, which pose practical challenges in applying physical models The Dark Object Subtraction (DOS), leveraging the intrinsic spectral characteristics of the imagery, offers an efficient alternative for achieving improved atmospheric correction results tailored to the data and study area. In this context, this study presents a Dark Object Subtraction for Water body information extraction (DOSW), specifically designed to advance the quantification of water body information in Zhuhai-1 hyperspectral data. The proposed method is rigorously evaluated by comparing the correction results from the Foshan region and Feilaixia Reservoir with standard and measured spectra of typical objects. The results demonstrate the accuracy of DOSW in atmospheric correction, with correlation coefficients exceeding 0.7 when compared to standard spectra for three representative objects. Notably, DOSW achieves exceptional accuracy in water body correction, achieving a correlation coefficient of 0.95 and an RMSE of 0.002 in the Feilaixia Reservoir, and a correlation coefficient of 0.72 and an RMSE of 0.005 in the Foshan region. Overall, the results underscore the efficacy of DOSW in accurately addressing atmospheric correction challenges to Zhuhai-1 hyperspectral data, effectively meeting the requirements of hyperspectral quantification applications.

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. The proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

Agricultural Stress Monitoring using Remote Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

Abstract. Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral water-leaving reflectance from HICO™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

Improvement of dark object method in atmospheric correction of hyperspectral remotely sensed data

The existing methods in atmospheric correction of hyperspectral data usually focus on removing the effects of water vapor and other absorptive gases, while this paper mainly studies the method of removing the influence of the aerosol and the water vapor simultaneously. Because the hyperspectral data has a larger number of bands, the conventional dark object method cannot be applied to the atmospheric correction of the hyperspectral data which can be improved, as described in this paper, by adequately making use of spectral characteristics of the hyperspectral data with an iterative correction during the whole process. The effects of the aerosol and water vapor are eliminated at the same time finally. The improved dark object method is used to do the atomospheric correction of the Hyperion data in Yanzhou, Shandong Province as an example. And the result indicates that it can correct the atmospheric influence of the hyperspectral data quickly and remarkably.

Remote Sensing Technology and Applications

A long anticipated new era in remote sensing has begunwith data now being acquired by commercially-ownedsatellites and airplanes as well as ~as in the past!government-owned satellites. The advent of this era ismarked by new imaging sensors and platforms includingthe first commercial high-resolution satellites. These pro-vide better than 1 meter ground sample distance. Thereare several commercial systems, including IKONOS,Quickbird and EROS, providing an unprecedented qualityand quantity of earth observation data in a very short timeafter acquisition! In addition, data can be readily pur-chased commercially from many other systems. Thetimely availability of this data opens up exciting new ap-plication areas including disaster management, urbanplanning and many others. For the first time in the historyof remote sensing, there are systems and data currently inthe commercial marketplace which, previously, were un-available or available only to governments. The industryis maturing and this Remote Sensing special section ofOptical Engineering includes papers addressing many as-pects of the current state of remote sensing.This special section begins with an overview of theglobal emergence of commercial earth observation satel-lites and their impact on current and future availability ofdigital remote sensing imagery data. This impact is syn-ergistic with the rapidly expanding field of geospatial in-formation systems ~GIS! applications. Subsequent papersfocus on: 1! image collection, 2! image processing tools,and 3! applications using both aerial and satellite collec-tion platforms.In their overview paper, Baker, O’Connor, and Venzorprovide observations from their unique position at RANDon the emergence of commercially-operated high-resolution imaging satellites. Although capable of provid-ing continuous earth coverage of great information valueto GIS analysts, the high initial cost of satellites and com-petition with government-sponsored systems present chal-lenges for the private investor. Chief among these chal-lenges are the policies of world governments that permitfree public access to commercial imagery, as well as clearrules governing competition from government-sponsoredcompetitors. The chief benefit of a financially healthy in-ternational commercial remote sensing satellite industry isnothing less than a significant increase in global transpar-ency, with attendant benefits of timely environmentalmonitoring and common datasets accessible to all govern-ments, non-government organizations ~NGOs!, and aca-demic and commercial value-added services groups.The group of papers on image collection includes Mar-tin’s paper describing the image collection optimizationprocess. This optimization is crucial to maximize use ofexpensive remote sensing satellites. Gorin and Sharp eachpresent important enabling techniques for hyperspectralimaging ~HSI!, an exciting remote sensing image datasetthat promises significant increase in information content.Gorin’s paper deals with the optimization, for object de-tection and recognition, of atmospheric correction of hy-perspectral data in the thermal wave bands. His techniqueuses a new image assessment measure, the spectral simi-larity scale, to quantify the ‘‘goodness of fit’’ for spectralemissivity output from atmospheric correction. Sharp de-scribes a data compression technique to greatly reduce thevast amounts of data to be stored and transmitted fromHSI sensors having 2001 sensor bands recorded in eachimage pixel. She demonstrates the technique’s effective-ness on datasets from a number of operational HSI sen-sors.The image processing papers describe automated ap-proaches to extract information from imagery data and atechnique to protect the intellectual property rights of adigital image by embedding a digital watermark. Chengand Toutin describe an automated process of extracting