Web-Based Supervised Thematic Mapping

Authors: Javier Lozano Silva Marco Quartulli Igor García Olaizola Ekaitz Zulueta Guerrero Naiara Aginako Bengoa

Date: 31.05.2015

IEEE Selected Topics in Applied Earth Observations and Remote Sensing


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Abstract

We introduce a methodology for semiautomatic thematic map generation from remotely sensed Earth Observation raster image data based on user-selected examples. The methodology is based on a probabilistic k-nearest neighbor supervised classification algorithm. Efficient operation is attained by exploiting data structures for high-dimensional indexing. The methodology is integrated in a Web-mapping server that is coupled to an HTML supervision interface that supports interactive navigation as well as model training and tuning. Quantitative classification quality and performance measurements are extracted for real optical data with 0.25 m resolution on a highly diverse training area.

BIB_text

@Article {
title = {Web-Based Supervised Thematic Mapping},
journal = {IEEE Selected Topics in Applied Earth Observations and Remote Sensing},
pages = {2165-2176},
number = {5},
volume = {8},
keywds = {

data structures, geophysics computing, hypermedia markup languages, remote sensing, Earth Observation raster image data, HTML supervision interface, Web-based supervised thematic mapping, data structure, feature
}
abstract = {

We introduce a methodology for semiautomatic thematic map generation from remotely sensed Earth Observation raster image data based on user-selected examples. The methodology is based on a probabilistic k-nearest neighbor supervised classification algorithm. Efficient operation is attained by exploiting data structures for high-dimensional indexing. The methodology is integrated in a Web-mapping server that is coupled to an HTML supervision interface that supports interactive navigation as well as model training and tuning. Quantitative classification quality and performance measurements are extracted for real optical data with 0.25 m resolution on a highly diverse training area.


}
isi = {1},
doi = {10.1109/JSTARS.2015.2438034},
date = {2015-05-31},
year = {2015},
}
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