Comparison between empirical and physically based models of atmospheric correction

Mandanici, Emanuele; Franci, Francesca; Bitelli, Gabriele; Αγαπίου, Άθως; Alexakis, Dimitrios D.; Hadjimitsis, Diofantos G.

doi:10.1117/12.2193176

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…The scarcity of dense vegetation in semiarid Mediterranean territories jeopardizes the effectiveness of this method and explains the poor performance in accuracy and separability for almost every class. Although we verified a good performance for impermeable surfaces, as was found in [14], our results for bare soil were much worse than theirs. This may be due to the existence of sparse vegetation, the diversity of mineral composition or the colour of the bedrock over bare soil surfaces.…”

Section: Discussionsupporting

confidence: 58%

“…Spectral signatures corrected with DOS were very similar to the uncorrected one, as in [14,19]. Image-based methods rely ultimately on the subtraction of dark dense vegetation (DDV) pixels from TOA data, so the shape of spectral signatures may no differ from data without correction.…”

Section: Discussionmentioning

confidence: 69%

“…A good pre-processing is essential for multi-temporal classification and retrieval of surface biophysical parameters from optical images [14]. Atmospheric correction (AC) is the most important step in this process, as most satellites currently include good quality radiometric and geometric corrections.…”

Section: Introductionmentioning

confidence: 99%

“…For these reasons, researchers from many institutions across Europe have developed a wide range of algorithms for atmospheric correction [21]. In recent years, several studies have been conducted to compare the reflectivity of Level-2A (L2A) images, processed with different AC algorithms, with ground truth reflectivity [14,19,22]. In addition, NASA and ESA, in the framework of the Committee on Earth Observation Satellites (CEOS), have initiated an Atmospheric Correction Inter-Comparison Exercise (ACIX) [23] to analyse several current AC algorithms and to compare the quality of their surface reflectance products.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

2021

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Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.

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Section: Discussionsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

2021

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“…The key question then here is: can DOS correction identify some facies better than others? Mandanici et al [29] compared the QUAC, FLAASH, DOS, Empirical Line, and 6S methods and found that a single most accurate method could not be identified. Moreover, in their results, the methods performed variably across different classification methods.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Jawak¹,

Wankhede

Luis

et al. 2022

Remote Sensing

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Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes.

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A New Threshold Relative Radiometric Correction Algorithm (TRRCA) of Multiband Satellite Data

Novelli

Aguilar

Tarantino

2017

Intelligent Interactive Multimedia Systems and Services 2017

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It is well known that remote sensed scenes could be affected by many factors and, for optimum change detection, these unwanted effects must be removed. In this study a new algorithm is proposed for PIF (Pseudo Invariant Features) extraction and relative radiometric normalization. The new algorithm can be labeled as a supervised one and combines three methods for the detection of PIFs: Moment distance index (MDI), Normalized Difference Vegetation Index (NDVI) masks morphological erosion and dilate operators. In order to prove its effectiveness, the algorithm was tested by using Landsat 8 scenes of the "Mar de Plstico" landscape of the Andalusian Almería. Many tests were performed in order to provide a set of valid input parameters for the chosen environments. Lastly, the results were statistically assessed with parametric and non-parametric tests showing very good and stable results in the four different study areas.

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Comparison between empirical and physically based models of atmospheric correction

Cited by 5 publications

References 7 publications

Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

A New Threshold Relative Radiometric Correction Algorithm (TRRCA) of Multiband Satellite Data

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