Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here  present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters.
['Gaohong Yin', 'Gregoire Mariethoz', 'Matthew F. McCabe']
I am writing a research paper on a idea Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method
[{"from": "human", "value": "I am writing a research paper on a idea Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here  present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters."}]