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Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Dimitris G. Stavrakoudis, Georgia N. Galidaki, Ioannis Z. Gitas, John B. Theocharis
2012 International Journal of Computational Intelligence Systems  
This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the  ...  and compact fuzzy rule-based classifiers, even for very high-dimensional feature spaces.  ...  Particularly, in land cover classification of forests, where typically different species of the same genus coexist, it has been shown that hyperspectral satellite imagery can significantly increase the  ... 
doi:10.1080/18756891.2012.685290 fatcat:d4rcvwjw3vh6blavulq5o33zfq

Support vector machines in remote sensing: A review

Giorgos Mountrakis, Jungho Im, Caesar Ogole
2011 ISPRS journal of photogrammetry and remote sensing (Print)  
A wide range of methods for analysis of airborne-and satellite-derived imagery continues to be proposed and assessed.  ...  A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010).  ...  Ghoggali and Melgani (2008) integrated genetic training into SVM classification in order to incorporate land cover transition rules in multitemporal classification.  ... 
doi:10.1016/j.isprsjprs.2010.11.001 fatcat:6hx57jxaxvfxvjoqqmhk5puhty

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

Dimitris Stavrakoudis, Eleni Dragozi, Ioannis Gitas, Christos Karydas
2014 Remote Sensing  
Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM).  ...  This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping  ...  The EO-1 Hyperion data used were available at no cost from the US Geological Survey.  ... 
doi:10.3390/rs6086897 fatcat:xsjbrwlowva6hlelp65hcre5ee

Remote sensing imagery in vegetation mapping: a review

Y. Xie, Z. Sha, M. Yu
2008 Journal of Plant Ecology  
Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.  ...  This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.  ...  A fuzzy classification approach is usually useful in mixed-class areas and was investigated for the classification of suburban land cover from remote sensing imagery (Zhang and Foody 1998) , the study  ... 
doi:10.1093/jpe/rtm005 fatcat:z22ciuewtzctbdv34w5j5axpye

Spatial data mining on remote sensing perspective

2016 International Journal of Latest Trends in Engineering and Technology  
For detecting land cover change by analyzing the Landsat imagery over a period of 1986 and 2001, J.R. Otukei and T.  ...  Hongtau Hu and Yifang Ban (2008) classified the urban land-cover and detected the changes in land-cover over a period of 1988 to 2002 in the urban area of Beiging, China, using neural networks and rule  ... 
doi:10.21172/1.74.036 fatcat:mdrfpf5gefg7lojxpmknvcloym

Application of soft computing techniques in coastal study – A review

G.S. Dwarakish, B. Nithyapriya
2016 Journal of Ocean Engineering and Science  
Those maps can serve as a base for planning management activities.  ...  Soft computing techniques like Fuzzy Logic, Artificial Neural Network, Genetic Algorithm and Support Vector Machine are upcoming soft computing algorithms that find its application in classification, regression  ...  Genetic Algorithm (GA) which depicts the natural evolution can be used for optimizing the fuzzy rules which in turn provides good classification result [81] .  ... 
doi:10.1016/j.joes.2016.06.004 fatcat:557fox4a4vdglbcpggdxnlyrsa

A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data

Behnaz Bigdeli, Farhad Samadzadegan, Peter Reinartz
2013 Journal of the Indian Society of Remote Sensing  
This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system.  ...  Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery.  ...  Discussion and Conclusion In this paper, the performance of a SVM based multiple classifier system for classification of hyperspectral imageries is assessed.  ... 
doi:10.1007/s12524-013-0286-z fatcat:xlbisnt5wjdanpjye4asmbkp4a

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 914-927 Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers.  ...  ., +, JSTARS 2020 4973-4987 Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

A Survey of Various Algorithms Used on Multispectral Satellite Image Classification of Alwar Image Dataset

K. Joshil Raj, S. SivaSathya
2016 Indian Journal of Science and Technology  
The data is classified into classes of major land-use types.  ...  Findings: The study has taken into account various strategies used by different researchers to produce the best classification accuracies for classifying a satellite image dataset.  ...  Genetic search process extracts 15 land-cover classification rules for classification. Per-Pixel Classification Dataset 1 -98.7%. Dataset 2 -73.3%.  ... 
doi:10.17485/ijst/2016/v9i45/105304 fatcat:bpv7getqejbvzgml5yom5awuya

2012 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 50

2012 IEEE Transactions on Geoscience and Remote Sensing  
., +, TGRS Jan. 2012 149-169 Fuzzy systems A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery. Stavrakoudis, D.  ...  ., +, TGRS June 2012 2287- 2302 A Genetic Fuzzy-Rule-Based Classifier for Land Cover Classification From Hyperspectral Imagery. Stavrakoudis, D.  ...  Radar resolution A Novel Method for Imaging of Group Targets Moving in a Formation. Bai, X., +, TGRS Jan. 2012  ... 
doi:10.1109/tgrs.2012.2229656 fatcat:hjrotpfsqzhxlnnme27ftv33cu

A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral

Hongmin Gao, Lizhong Xu, Chenming Li, Aiye Shi, Fengchen Huang, Zhenli Ma
2013 Mathematical Problems in Engineering  
This paper proposes a hybrid feature selection strategy based on the simulated annealing genetic algorithm (SAGA) and the Choquet fuzzy integral (CFI).  ...  Rich spectral information from hyperspectral images can aid in the classification and recognition of the ground objects.  ...  A semisupervised method for addressing a domain adaptation problem based on multiple-kernel SVMs in the classification of hyperspectral data was presented in [6] .  ... 
doi:10.1155/2013/537268 fatcat:2vyckuid5zernmt533qwolmdmq

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Homayouni 6308 Processing, Sensors, and System for: Hyperspectral Data Nonlinear Endmember Identification for Hyperspectral Imagery via Hyperpath-Based Simplex Growing and Fuzzy Assessment . . . . . .  ...  A. van Aardt 1761 A Physical-Based Algorithm for Retrieving Land Surface Temperature From Moon-Based Earth Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Delp 5872 A Framework for Land Use Scenes Classification Based on Landscape Photos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

Front Matter: Volume 9808

2015 International Conference on Intelligent Earth Observing and Applications 2015  
using a Base 36 numbering system employing both numerals and letters.  ...  Publication of record for individual papers is online in the SPIE Digital Library. Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  -507] 9808 2O Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China [9808-273] vii Proc. of SPIE Vol. 9808 980801-7 The application of  ... 
doi:10.1117/12.2229161 fatcat:oh2qm3ogkvaddlh3llvltqps54

Attraction-Repulsion Model-Based Subpixel Mapping of Multi-/Hyperspectral Imagery

Xiaohua Tong, Xue Zhang, Jie Shan, Huan Xie, Miaolong Liu
2013 IEEE Transactions on Geoscience and Remote Sensing  
This paper presents a new subpixel mapping method based on subpixel attraction-repulsion.  ...  The proposed method is formulated as an optimization problem with respect to attractionrepulsion among subpixels and is used to reconstruct a finer spatial resolution image from a lower resolution one.  ...  Zhang was a visiting student at Purdue University under the support of China Scholarship Council Scholarship between 2009 and 2010.  ... 
doi:10.1109/tgrs.2012.2218612 fatcat:gm2hr6ofzve7reb7tn2ajijymq


H. T. Chu, L. Ge
2012 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The integration of different kinds of remotely sensed data, in particular Synthetic Aperture Radar (SAR) and optical satellite imagery, is considered a promising approach for land cover classification  ...  proposed and evaluated for classifying land cover features in New South Wales, Australia.  ...  Foody et al. (2007) integrated five classifiers based on majority voting rule for mapping fenland East Anglia, UK.  ... 
doi:10.5194/isprsarchives-xxxix-b7-173-2012 fatcat:xbcnyojdrfh7pfpp5ddhwpb3pa
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