Nécessità de l'étalonnage radiométrique et standardisation des images numériques de télédétection

Bannari, A.; Teillet, Philippe; Richardson, Guillermo Videla

doi:10.1080/07038992.1999.10855262

Cited by 28 publications

(16 citation statements)

References 49 publications

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“…According to the OLI image preprocessing steps, we consider sensor radiometric drift, atmospheric corrections, and geometric and topographic rectification. Drift of the sensor radiometric calibration is a necessary step, which consists of correcting artifacts affecting the sensor in order to extract precise and reliable information from an image [24]. Relative calibration is a normalization and harmonization of the data received from the different detectors of OLI sensor.…”

Section: Oli Landsat-8 Data and Processingmentioning

confidence: 99%

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

El-Battay¹,

Bannari²,

Hameid³

et al. 2017

ARS

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Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity 24validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

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Section: Oli Landsat-8 Data and Processingmentioning

confidence: 99%

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

El-Battay¹,

Bannari²,

Hameid³

et al. 2017

ARS

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“…Drift of the sensor radiometric calibration (relative and absolute) is a necessary step, which consists of correcting artifacts affecting the sensor in order to extract precise and reliable information from an image [30]. Relative calibration is a normalization and harmonization of the data received from the different detectors of TM sensor.…”

Section: Optic Data Surface Reflectance Retrievalmentioning

confidence: 99%

A fusion approach of PALSAR-FBS L-HH mode and TM data for geological mapping

Bannari

Saquaque²,

Miri³

et al. 2018

International Conference on Intelligent Earth Observing and Applications 2015

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This paper presents a fusion approach of PALSAR-FBS L-HH polarization and Landsat-5 TM datasets for geological mapping and morpho-structural lineaments extraction. The study site is situated in Western Ethiopia, BenshangulGumuz National Regional State, Asosa Zones; which is characterized by lithological diversity and rich mineral resources. The TM data were calibrated radiometrically and corrected atmospherically to retrieve ground surface reflectance. As well, the radar data were calibrated to retrieve backscatter coefficients and then filtered using the gamma filter to reduce speckle. Moreover, the two images were geometrically corrected and topographically rectified using the ASTER GDEM, UTM projection and WGS84 geodetic reference. Then, the images were transformed to Intensity-HueSaturation (I-H-S) using three different methods, such as hexagonal, double hexagonal and cylindrical transformations. Based on several tests integrating all the considered datasets, the SWIR and NIR bands were selected for "I", "H" and "S" codification using cylindrical transformation. Three color composites (named spatio-maps) were selected due to their best results: 1) "I", "H" and "L-HH"; 2) "I", "H" and the blue band resulting from the fusion of TM (7, 5, 4) and LHH; and 3) the R-G-B resulting from the fusion of "I", "H", "S" and "L-HH". These derived spatio-maps show good relationship between the topographic morphology, rock-substrate, structural variations properties, and drainage network. In addition, the spectral variations are easily associated with lithological units. Likewise, the morpho-structural information's highlighted in the PALSAR image are visible without altering the radiometric integrity of the details in TM spectral bands through the fusion process. Otherwise, the synergy between visual and automatic methods for lineaments extraction provides the best lineaments maps. The obtained product shows that the predominant lineaments directions are the NE-SW and the NS, and then the second dominant direction is the NW-SE. The results integration in GIS environment provides good discrimination of fractures and details of structural attributes. This research results highlight the importance of the PALSAR fine mode L-HH and TM data fusion to enhance geological features.

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“…Dans plusieurs genres d'études, la calibration radiométrique absolue n'est pas nécessaire, comme la cartographie de zones forestières et de champs (King et Vlcek, 1990et Everitt et al, 1992. D'autre part, pour l'étude à moyen et long terme de ces mêmes zones où une évolution dans la détection est désirée, une imagerie radiométriquement juste devrait être utilisée de manière à quantifier convenablement les changements possibles (Neale et al, 1995, Bannari et al, 1998. Le système que l'on doit employer doit donc tenir compte de la variation de la lumière incidente.…”

Section: Simulation Duunclassified

Mise au point d'un reflectomètre imageur à partir de la vidéographie aérienne multispectrale (VAM) /

Gastonguay¹

2002

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UIUQAC Mise en garde/AdviceAfin de rendre accessible au plus grand nombre le résultat des travaux de recherche menés par ses étudiants gradués et dans l'esprit des règles qui régissent le dépôt et la diffusion des mémoires et thèses produits dans cette Institution, l'Université du Québec à Chicoutimi (UQAC) est fière de rendre accessible une version complète et gratuite de cette oeuvre.Motivated by a desire to make the results of its graduate students' research accessible to all, and in accordance with the rules governing the acceptation and diffusion of dissertations and theses in this Institution, the Université du Québec à Chicoutimi (UQAC) is proud to make a complete version of this work available at no cost to the reader.L'auteur conserve néanmoins la propriété du droit d'auteur qui protège ce mémoire ou cette thèse. Ni le mémoire ou la thèse ni des extraits substantiels de ceux-ci ne peuvent être imprimés ou autrement reproduits sans son autorisation.The author retains ownership of the copyright of this dissertation or thesis. Neither the dissertation or thesis, nor substantial extracts from it, may be printed or otherwise reproduced without the author's permission. II RÉSUMÉEn télédétection aérienne, on peut obtenir des images du sol avec différents types de capteurs, justifiés par l'usage que l'on souhaite faire des images. Le système utilisé par le laboratoire de télédétection aérienne de l'UQAC est la vidéographie aérienne multispectrale (VAM). Ce système de caméra peut capter indépendamment jusqu'à 6 bandes spectrales, réparties du violet au proche infrarouge. Un système comme celui-ci peut donc nous permettre d'obtenir de l'information au sol dans des plages de longueurs d'onde bien définies, ce qui simplifie l'étude de certains aspects du sol.L'usage d'un capteur sensible au proche infrarouge est particulièrement intéressant pour étudier les stades phénologiques des végétaux. Ceux-ci peuvent se discriminer en observant la variation temporelle de l'albédo dans cette bande, caractéristique de la chlorophylle qui est produite par les végétaux.La télédétection aérienne avec la bande proche infrarouge est donc souvent employée pour faire le suivi dans le temps d'un secteur particulier. Prenons par exemple des zones dévastées par des feux de forêt ou encore des zones agricoles. H faut alors faire plusieurs survols espacés temporellement d'un même endroit pour constater s'il y a eu ou non évolution de l'albédo des végétaux observés.Malheureusement, il était impossible avec le système original de constater une évolution de l'albédo car il n'y avait pas d'appareils pouvant nous assurer que les observations avaient été faites dans les mêmes conditions de lumière incidente. La variation dans l'intensité de signal alors reçu au capteur dépendait beaucoup plus de la lumière incidente que de la variation de l'albédo.La comparaison directe d'imagerie aérienne ne devrait alors être faite que lorsque l'on sait que la lumière ambiante disponible était la même au moment de la prise d'information. Malheureusement, ce n...

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Nécessità de l'étalonnage radiométrique et standardisation des images numériques de télédétection

Cited by 28 publications

References 49 publications

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

A fusion approach of PALSAR-FBS L-HH mode and TM data for geological mapping

Mise au point d'un reflectomètre imageur à partir de la vidéographie aérienne multispectrale (VAM) /

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