Automated Geometric Correction of Landsat MSS L1G Imagery

Devaraj, Chabitha; Shah, Chintan

doi:10.1109/lgrs.2013.2257677

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…This system of equations holds when images are acquired from the same observation angle, as exemplified in Equation (9). For acquisitions that are matched from different orbits, an additional configuration matrix needs to be constructed to formulate the orthorectification offset based on Equation (9);…”

Section: Parameter Estimationmentioning

confidence: 99%

“…However, in many cases there will be a difference between the DEM used for orthorectification and the true elevation at acquisition time as glaciers are dynamic topographic features that may exhibit vertical changes of many meters per year. Other methodologies to treat orthorectification offsets have been proposed, but these focused on improving the miss-alignment, assuming stable terrain [7][8][9][10]. In the complicated case of a changing topography, an independent, self-reliant method is preferred, to limit the introduction of varying bias through the additional elevation data sources used for terrain corrections.…”

Section: Introductionmentioning

confidence: 99%

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Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Altena

Kääb

2017

Remote Sensing

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Abstract:Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources with variable qualities, from dispersed time instances, or with different spatial resolutions. Consequently, terrain representation used for orthorectifying satellite images is often in disagreement with reality at image acquisition. Normally, the lateral orthoprojection offsets resulting from vertical DEM errors are taken into account in the geolocation error budget of the corrected images, or may even be neglected. The largest offsets of this type are often found over glaciers, as these may show strong elevation changes over time and thus large elevation errors in the reference DEM with respect to image acquisition. The detection and correction of such orthorectification offsets is further complicated by ice flow which adds a second offset component to the displacement vectors between orthorectified data. Vice versa, measurement of glacier flow is complicated by the inherent superposition of ice movement vectors and orthorectification offset vectors. In this study, we try to estimate these orthorectification offsets in the presence of terrain movement and translate them to elevation biases in the reference surface. We demonstrate our method using three different sites which include very dynamic glaciers. For the Oriental Glacier, an outlet of the Southern Patagonian icefield, Landsat 7 and 8 data from different orbits enabled the identification of trends related to elevation change. For the Aletsch Glacier, Swiss Alps, we assess the terrain offsets of both Landsat 8 and Sentinel-2A: a superior DEM appears to be used for Landsat in comparison to Sentinel-2, however a systematic bias is observed in the snow covered areas. Lastly, we demonstrate our methodology in a pipeline structure; displacement estimates for the Helheim-glacier, in Greenland, are mapped and corrected for orthorectification offsets between data from different orbits, which enables a twice as dense a temporal resolution of velocity data, as compared to the standard method of measuring velocities from repeat-orbit data only. In addition, we introduce and implement a novel matching method which uses image triplets. By formulating the three image displacements as a convolution, a geometric constraint can be exploited. Such a constraint enhances the reliability of the displacement estimations. Furthermore the implementation is simple and computationally swift.

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Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Altena

Kääb

2017

Remote Sensing

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“…The image processing was done using ENVI Software. First, the image was corrected using Geometric correction to ensure that the data is accurately spatially aligned [10]. The process entails detecting and correcting distortions caused by changes in the position and orientation of the sensor platform, as well as terrain and atmospheric effects.…”

Section: ) Satellite Image Processingmentioning

confidence: 99%

Remote Sensing Based Modeling of Land Use Change on Ulayat Customary Land in Tilatang Kamang Sumatera Barat

Ikhwan,

Barlian,

Syah

2023

TEM Journal

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Ulayat land is traditional West Sumatra land frequently subject to competing demands from various stakeholders, including government agencies, private companies, and local communities. Those demands lead to the need to update geospatial data in the form of land use modeling for further land management purposes. This research used remote sensing technology to develop land use and land cover models based on cellular automata and socio-economic data. This research shows that there will be a significant increase in population in 2030, followed by changes in residential land use. The land use model shows that the use of customary land in most areas is not followed by a change in ownership which shows that tribe rules are still being followed and maintained correctly.

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“…These process correct the data for sensor irregularities and removing (radiometric corrections) unwanted sensor distortion or atmospheric noise. Landsat MSS L1G ( Multispectral Scanners Landsat 1 Generation) images [14] are used to analyze multi-temporal changes and should be free from geometric distortions. DEM (Digital Elevation Model) and MSS sensor parameters are used for ortho-rectification and geo-referencing to improve the accuracy of geometric corrections, proposed method is evaluated quantitatively and has high accuracy.…”

Section: Pre-processingmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth's surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.

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Automated Geometric Correction of Landsat MSS L1G Imagery

Cited by 12 publications

References 11 publications

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Remote Sensing Based Modeling of Land Use Change on Ulayat Customary Land in Tilatang Kamang Sumatera Barat

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

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