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Digital image processing of earth observation sensor data
- IBM Journal of Research and Development
, 1976
"... Abstract: This paper describes digital image processing techniques that were developed to precisely correct Landsat multispectral Earth observation data and gives illustrations of the results achieved, e.g., geometric corrections with an error of less than one picture element, a relative error of on ..."
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Abstract: This paper describes digital image processing techniques that were developed to precisely correct Landsat multispectral Earth observation data and gives illustrations of the results achieved, e.g., geometric corrections with an error of less than one picture element, a relative error of one-fourth picture element, and no radiometric error effect. Techniques for enhancing the sensor data, digitally mosaicking multiple scenes, and extracting information are also illustrated.
Landsat-8: Science and product vision for terrestrial global change research
- Remote Sens. Environ. 2014
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Remote Sensing of Environment
"... hat allows collection of a 2–2017) Landsat Science s ahead in support of pri-tification of new science ..."
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hat allows collection of a 2–2017) Landsat Science s ahead in support of pri-tification of new science
Article A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)
"... www.mdpi.com/journal/remotesensing ..."
1 A High Resolution Satellite Interpretation Technique for Crop Area Monitoring in Developing Countries
"... District-level crop area (CA) is a highly uncertain term in food production equations, which are used to allocate food aid and implement appropriate food security initiatives. Remote sensing studies typically overestimate CA and production, as subsistence plots are exaggerated at coarser resolution, ..."
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District-level crop area (CA) is a highly uncertain term in food production equations, which are used to allocate food aid and implement appropriate food security initiatives. Remote sensing studies typically overestimate CA and production, as subsistence plots are exaggerated at coarser resolution, which leads to over optimistic food reports. In this study, medium resolution Landsat ETM+ images were manually classified for Niger and corrected using CA estimates derived from high resolution sample image, topographic, and socioeconomic data. A logistic model with smoothing splines was used to compute the block-average (0.1 degree) probability of an area being cropped. Livelihood zones and elevation explained 75 % of the deviance in cropped area, while medium resolution did not add explanatory power. The model overestimates crop area when compared to the national inventory, perhaps due to temporal changes in intercropping, and the exclusion of some staple crops in the national inventory. 3
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"... Crop area estimation using high and medium resolution satellite imagery in areas with complex topography ..."
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Crop area estimation using high and medium resolution satellite imagery in areas with complex topography
