Multiple classifiers applied to multisource remote sensing data
نویسندگان
چکیده
منابع مشابه
Multiple classifiers applied to multisource remote sensing data
The combination of multisource remote sensing and geographic data is believed to offer improved accuracies in land cover classification. For such classification, the conventional parametric statistical classifiers, which have been applied successfully in remote sensing for the last two decades, are not appropriate, since a convenient multivariate statistical model does not exist for the data. I...
متن کاملHyperspectral Remote Sensing Data Mining Using Multiple Classifiers Combination
Since the advent of remote sensing in the second half of 20th century, nowadays there have been great changes in theory and technology. The advent of hyperspectral was one of the most significant breakthroughs in remote sensing. Hyperspectral remote sensing has higher spectral resolution as the same time retain higher spatial resolution, so its capability of distinguishing the different and des...
متن کاملMultisource Data Analysis in Remote Sensing and Geographic Information Processing
A general approach is presented for the computer analysis, using quantitative multivariate methods, of remote sensing data combined with other sources of data in geographic information systems. A method is proposed by which inferences can be drawn systematically from multiple observations having significant but unknown interactions. A simple classification experim~nt with Landsat MSS data is un...
متن کاملSuitability Evaluation for Products Generation from Multisource Remote Sensing Data
With the arrival of the big data era in Earth observation, the remote sensing communities have accumulated a large amount of invaluable and irreplaceable data for global monitoring. These massive remote sensing data have enabled large-area and long-term series Earth observation, and have, in particular, made standard, automated product generation more popular. However, there is more than one ty...
متن کاملEvaluation of Multiple Classifier Combination Techniques for Land Cover Classification Using Multisource Remote Sensing Data
Use of multisource remote sensing data, particularly Synthetic Aperture Radar (SAR) and optical images, can improve performance of land cover classification since many types of information are involved in the classification process. Recently, the multiple classification systems (MCS) or ensemble classifiers has been developed and increasingly used for classifying remote sensing data. It is cons...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2002
ISSN: 0196-2892
DOI: 10.1109/tgrs.2002.802476