Land Cover Feature Extraction using Hybrid Swarm
Intelligence Techniques - A Remote Sensing
Perspective
L. Goel. ACEEE International Journal on Signal & Image Processing, 1 (3):
3(December 2010)
Abstract
The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).
%0 Journal Article
%1 goel2010cover
%A Goel, Lavika
%D 2010
%E Das, Dr. Vinu V
%J ACEEE International Journal on Signal & Image Processing
%K Biogeography Image_Classification Remote_Sensing
%N 3
%P 3
%T Land Cover Feature Extraction using Hybrid Swarm
Intelligence Techniques - A Remote Sensing
Perspective
%U http://doi.searchdl.org/01.IJSIP.1.3.65
%V 1
%X The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).
@article{goel2010cover,
abstract = {The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).},
added-at = {2012-10-03T11:04:18.000+0200},
author = {Goel, Lavika},
biburl = {https://www.bibsonomy.org/bibtex/2cb1b4df28782ee8bc850c3bf5460ebba/ideseditor},
editor = {Das, Dr. Vinu V},
interhash = {a6ed431e384f96ac9c8de26b5a6f405b},
intrahash = {cb1b4df28782ee8bc850c3bf5460ebba},
journal = {ACEEE International Journal on Signal & Image Processing },
keywords = {Biogeography Image_Classification Remote_Sensing},
month = {December},
number = 3,
pages = 3,
timestamp = {2013-01-09T06:33:48.000+0100},
title = {Land Cover Feature Extraction using Hybrid Swarm
Intelligence Techniques - A Remote Sensing
Perspective},
url = {http://doi.searchdl.org/01.IJSIP.1.3.65},
volume = 1,
year = 2010
}