Adaptive Method for Landsat ETM+ Gap Filling Using Successive Temporal Images

Salman, Salema S.; Al-Saleh, Anwar H.; Abbas, Heba Kh.; Al-Zuky, Ali A.

doi:10.14704/nq.2020.18.2.nq20135

Cited by 3 publications

(7 citation statements)

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“…Unfortunately slightly slanting and distort the image from right to left of the images, these cropped images are shown in Figure (1). Where applying temporal filling gap algorithm [11] that uses three different temporal ETM+ images for each band in order to make an initial gap filling step : The gap-filled algorithm was efficient in processing many ETM + images and removing gaps lines completely. But in some cases, it did not succeed in completely removing the gaps, but after its implementation, a large proportion of the gap lines in the images were removed, but it left narrow gap lines and sometimes extended to the middle of the images.…”

Section: -Gap Scan Line Filling Methodologymentioning

confidence: 99%

“…However, this algorithm is not effective for eliminating gap lines when the temporal technique is not used to reduce the width of the gap lines and reduce a large portion of them. This is because if this technique is applied to remove the gap lines directly without using any other technique before it, it will lead to the appearing a high Therefore, in this study, morphology filtration technology was presented as an auxiliary technique aimed at removing any remaining lines from the first step processing technique (temporal algorthim) [11]. The applicability of filling in morphology has been tested until now across a maong of applications, which include the dilation, erosion, opening and closing methods [2].…”

Section: -Gap Scan Line Filling Methodologymentioning

confidence: 99%

“…Algorithm: The proposed morphological filtering methods to complete the gap SLC lines removal of the processed ETM+ images by applying Salema et.al method [11]: Input: The ETM+ Eight bands images (I1-I8)// obtained by Salema et al method [11] Output: Free gap lines ETM+ bands images K) . The Steps: 1-Calling band (I6 or I7) and converting into binary image (IB) to distinguish the points of gap-lines in the image and assign a zero to them, these images shown in fig ( 2: a).…”

Section: -The Proposed Algorithmmentioning

confidence: 99%

“…Salema S. Salman et al ,(2020) [11] , she embraced the criterion of correlation between satellite images to identifie spatially corresponding between images. where Three different multi temporal images were applied in filling the gaps , by use two algorithms.…”

mentioning

confidence: 99%

“…In this study, a technique for removing gap lines from ETM + images acquired at 2004 was developed, where the temporal image fusion technique presented by Salema et.al. [11] Was used to remove the SLC gap as a first stage in removing the gap lines. However, this technique fails in some cases.…”

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confidence: 99%

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Gap Lines Reduction for Enhaced Temporal Landsat Etm+ Imagery Using Morphological Oprations

عبطان¹,

كاظم2²,

صالح³

2022

cbej

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The failure of the Scanning Line Corrector of ETM+2004 of Landsat7 in imposed systematic data gap lines on retrieved the correct satellite images and rejected the ability to provide continuous information in different scientific fields. While a many processing algorithms proposed to fill out this gap lines, utmost of these algorithms are just only valid through the homogeneous image regions. When there are applied to process the heterogeneous ETM+landsat7 images, returning image pixels and image features can’t become correct and precise. In this study a gap filling algorithm has been proposed that is based on the morphologic operations of image. The operations used here are combination of the processes, dilation and erosion. The algorithm employs opening, closing morphologic operations of gap image methods to identify the properties and size the separated gab lines image for ETM+landsat7 images. Each band images has been passed through milt-image processing stages. The images that output of all stages are eight corrected images. Enhanced images are very clear and the gab lines completely disappear from all image bands. The algorithm succeeded in correction and filling gab lines for homogeneous and heterogeneous image areas.

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Section: -Gap Scan Line Filling Methodologymentioning

confidence: 99%

Section: -Gap Scan Line Filling Methodologymentioning

confidence: 99%

Section: -The Proposed Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Gap Lines Reduction for Enhaced Temporal Landsat Etm+ Imagery Using Morphological Oprations

عبطان¹,

كاظم2²,

صالح³

2022

cbej

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Algorithm Development for Full Gaps of Landsat 7 Images

Abd-Almajied

George

Hameed

2023

Iraqi Journal of Science

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Landsat7 of Enhanced thematic mapper plus (ETM+) was launched on April 15, 1999. Four years later, images start degrading due to the scan line corrector (SLC). SLC is a malfunction that results in pixel gaps in images captured by the sensor of Landsat7. The pixel gap regions extend from about one pixel near the image center and reach up to about 14 pixels in width near the image edge. The shape of this loss is like a zigzag line; however, there are different studies about repairing these gaps. The challenge of all studies depends on retrieving inhomogeneous areas because the homogenous area can be retrieved quickly depending on the surrounding area. This research focuses on filling these gaps by utilizing pixels around them gaps. Pixels from different satellites with the same resolution at the approximate equal date and exact location would be utilized. Mean and standard deviation were used in gap pixel predicting. For complete pixels gap predicting, an image registration should be applied for predicted a good result—image registration comprised of geometric and radiometric registration. Translation and rotation was the main effect of geometric registration, while pixel tone was mainly related to radiometric registration. Two images of Landsat8 (before and after) with nearly the date of Landsat 7 were used. The fidelity criteria (mean square error and mean absolute error) were utilized to evaluate the results. The results showed a good prediction of the gap pixels in the Landsat 7 image, considering the geometric and radiometric parts.

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Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI

Evans

Shen

2021

Remote Sensing

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Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.

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Adaptive Method for Landsat ETM+ Gap Filling Using Successive Temporal Images

Cited by 3 publications

References 5 publications

Gap Lines Reduction for Enhaced Temporal Landsat Etm+ Imagery Using Morphological Oprations

Gap Lines Reduction for Enhaced Temporal Landsat Etm+ Imagery Using Morphological Oprations

Algorithm Development for Full Gaps of Landsat 7 Images

Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI

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