Pelgrum, H., 2000: Spatial aggregation of land surface characteristics: impact of resolution of remote sensing data on land surface modelling (Spatial aggregation of land surface characteristics: impact of resolution of remote sensing data on
This paper describes the application of remote sensing techniques, based on SAR interferometry for the intensity zonation of the landslide affecting the Castagnola village (Northern Apennines of Liguria region, Italy).
At CNES, the prototype MUSCATE production facility of THEIA land data centre will soon start the production and distribution of Level 2A Landsat 5 and 7 data, and shortly after of LANDSAT 8 data covering the entire surface ...
As emerging space pioneers in China and India race to the moon and Mars, private-sector startups are quietly launching satellites the size of microwave ovens that aim to satisfy much more terrestrial desires.
Every eight days, a Landsat satellite carrying delicate sensing and scanning equipment passes high above wherever you might be on the planet. Data from Landsat mission images records changes on Earth’s surface since 1972, over the last four decades, and it’s freely available.
The terms GIS (which most commonly is an acronym for Geographic Information Systems) and geospatial are often used interchangeably. There are differences in what the terms GIS and geospatial mean. What is GIS?
Just like a physician measures a person’s temperature in order to check his or her health, a new project from the European Space Agency (ESA) hopes to track temperatures of the Earth’s surface by satellite.
PURPOSE: To provide an overview of principles involved in remote sensing of coastal wave data using radar and to summarize several systems that are (Remote Sensing Techniques for Measuring Ocean Waves.: PURPOSE: To provide an overview of...
Abstract: Although a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative) training samples than others. Many algorithms improved the overall accuracy marginally with per-segment decision making.Keywords: machine learning; maximum likelihood classification; logistic regression; support vector machine; tree classifiers; random forests
As Bruno described last month, we leverage the Landsat Ecosystem Disturbance Adaptive Processing System, or LEDAPS, software routines to process Landsat Level 1 images to surface reflectance. This conversion is...
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