Relative Radiometric Correction of Pushbroom Satellites Using the Yaw Maneuver

Begeman, Christopher; Helder, Dennis L.; Leigh, Larry; Pinkert, Chase

doi:10.3390/rs14122820

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Radiometric correction addresses the worsening of image quality due to reflection errors or other factors that are very useful for multi-temporal and multi-sensor data analysis [65,66]. The radiometric correction process is performed by converting the digital number of an image into a reflectance value [67]. Atmospheric correction is a fundamental pre-analysis stage in any quantitative analysis that aims to eliminate atmospheric effects [68,69].…”

Section: Pre-processingmentioning

confidence: 99%

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

et al. 2022

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Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI+NDSI+DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI+NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI+NDSI+DEM parameters with the number of trees = 100, mtry = all variables (, and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

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Section: Pre-processingmentioning

confidence: 99%

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

et al. 2022

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“…The atmospheric correction, performed using the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube) algorithm, is necessary for the pre-processing of satellite images. It is applied to Landsat-8 images with atmospheric and tropospheric aerosol models of mid-latitudes [29]. It is considered as an imaging spectrometer rescaled in reflectance units for raw radiance data from atmospheric correction [30] and used to isolate the characteristics of the observation, which are intrinsic to the surface, from those caused by the atmosphere [31].…”

Section: (C) Atmospheric Correctionmentioning

confidence: 99%

Remote Sensing Data for Geological Mapping in the Saka Region in Northeast Morocco: An Integrated Approach

et al. 2022

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Together with geological survey data, satellite imagery provides useful information for geological mapping. In this context, the aim of this study is to map geological units of the Saka region, situated in the northeast part of Morocco based on Landsat Oli-8 and ASTER images. Specifically, this study aims to: (1) map the lithological facies of the Saka volcanic zone, (2) discriminate the different minerals using Landsat Oli-8 and ASTER imagery, and (3) validate the results with field observations and geological maps. To do so, in this study we used different techniques to achieve the above objectives including color composition (CC), band ratio (BR), minimum noise fraction (MNF), principal component analysis (PCA), and spectral angle mapper (SAM) classification. The results obtained show good discrimination between the different lithological facies, which is confirmed by the supervised classification of the images and validated by field missions and the geological map with a scale of 1/500,000. The classification results show that the study area is dominated by Basaltic rocks, followed by Trachy andesites then Hawaites. These rocks are encased by quaternary sedimentary rocks and an abundance of Quartz, Feldspar, Pyroxene, and Amphibole minerals.

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“…The AMETHIST method was first tested on the Pleiades satellite [21], [22]. Similarly, the GaoFen-9 [23], [24] and Landsat-8 [25], [26] satellites have adopted the approach of rotating the camera by 90° to perform RRC, with promising results. Since this method does not depend on ground or uniform calibration scenes, high calibration accuracy and strong timeliness are ensured.…”

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confidence: 99%

An Improved Side-Slither Relative Radiometric Calibration Method for WFV Satellite: Taking HY1D CZI as an Example

Chen,

Wang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wide field of view (WFV) push-broom optical satellites can acquire ground images over hundreds to thousands of kilometers in a single pass, thanks to their large field of view cameras consisting of tens or even hundreds of thousands of detectors, which also makes their relative radiometric correction (RRC) difficult. Existing side-slither-based RRC approaches overlook the distinct structural design of WFV push-broom optical satellite cameras, thus failing to rectify non-linear distortions in side-slither images stemming from the non-flat arrangement of camera detectors. Furthermore, these methods necessitate corresponding pixels (pixels of same ground object) covering the entire field of view (FOV) in side-slither images. However, the brief duration of side-slither imaging makes it unfeasible to cover the entire FOV with calibration data. To address these issues, we propose a novel RRC approach for WFV push-broom optical satellites, achieved based on a thorough analysis of the unique structural traits of WFV push-broom optical cameras, enabling precise standardization of the non-linear distortions in side-slither data. Additionally, a local-to-global side-slither calibration strategy is proposed to obviate the requirement for corresponding pixels to cover the entire FOV in calibration data. Experiments using the Haiyang-1D Coastal Zone Imager (HY-1D CZI) satellite indicate that our method effectively rectified non-linear distortions in side-slither data and evident RRC accuracy improvement with that of existing methods can be obtained.Index Terms-relative radiometric calibration (RRC), sideslither, wide field of view, HY-1D CZI I. INTRODUCTION emote-sensing satellites have become an indispensable means of acquiring extensive ground information, with the quality of their image products largely depending on the design of the optical imaging system and subsequent radiometric calibration. Nowadays, charge-coupled devices (CCDs) are frequently integrated into optical imaging systems, offering push-broom and whisk-broom imaging modes as alternatives. In practice, push-broom optical sensors are predominant in this field. Linear array push-broom sensors are composed of a group of independent detectors. When provided with the same radiance input, the detectors should ideally yield Manuscript received XXX. revised XXX, XXX.

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Relative Radiometric Correction of Pushbroom Satellites Using the Yaw Maneuver

Cited by 5 publications

References 10 publications

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Remote Sensing Data for Geological Mapping in the Saka Region in Northeast Morocco: An Integrated Approach

An Improved Side-Slither Relative Radiometric Calibration Method for WFV Satellite: Taking HY1D CZI as an Example

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