ماردة
" ثـــــــــــــــــــــــــائــــــــــر "
عدد الرسائل : 201
تاريخ التسجيل : 26/10/2010
وســــــــــام النشــــــــــــــاط : 2
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 | People
Papers Organizations
- JPL Section 388
- Univ. of CO, Denver
- Univ. of NM
Sponsors
| News
Our paper on predicting crop yield using a multiple-instance regression approach, "Multiple-Instance Regression with Structured Data", was accepted to the 4th International Workshop on Mining Complex Data and will be presented in December, 2008 (Pisa, Italy). In September 2007, we made our final delivery of PixelLearn to the USDA's United States Salinity Laboratory. They are conducting a study that involves connecting ground estimates of soil salinity with orbital remote sensing data, and PixelLearn now provides regression algorithms to accomplish this goal.
About HARVIST
Remote sensing instruments in Earth orbit provide a rich source of information about current agricultural conditions. Observed over time, patterns emerge that can assist in the prediction of future conditions, such as the yield expected for a given crop at the end of the growing season. It is suspected that these predictions can be made more accurate by incorporating other sources of information, such as weather conditions from ground stations, soil properties, etc. The tools required to access and combine large amounts of data from multiple sources, at different spatial resolutions, are not readily available. The HARVIST (Heterogeneous Agricultural Research Via Interactive, Scalable Technology) project seeks to address this lack by demonstrating the technology required to perform large scale studies of the interactions between agriculture and climate. Our goal is to integrate multiple Earth Science data sources into a single graphical user interface that allows for the investigation of connections between different variables. In particular, we focus on relationships between weather and crop yield, but the system we are creating will be capable of integrating data for other studies as well. The data sources are heterogeneous in that they contain information at different spatial, spectral, and temporal resolutions. Specifically, we aim to combine support vector machines (SVMs; classification), clustering (discovery), and multivariate spatial modeling (regression and prediction) methods into a single, interactive package to explore the impact of variables on crop yield.
PixelLearn
HARVIST uses the graphical PixelLearn system to conduct perform interactive data labeling and analysis. In the screenshot below, a remote sensing image of California's Central Valley is shown. The user has labeled several pixels in the left panel by "painting" colored labels on them. Here, green indicates vegetation, blue is water, and black is land. After training an SVM classifier, the output is shown in the right, in which every pixel in the image has been assigned to the class that best describes it. The user can iterate, labeling more pixels and examining the new SVM output, until the result is satisfactory. PixelLearn also provides data clustering and regression capabilities.
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