EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion

Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolut...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2012, Vol.75 (1), p.115-122
Main Authors: García-Gutiérrez, Jorge, Mateos-García, Daniel, Riquelme-Santos, José C.
Format: Article
Language:English
Subjects:
Artificial intelligence
Classification
Data fusion
Data integration
Ensembles
Evolutionary computation
Expert systems
Feature weighting
Hybrid artificial intelligence systems
Label dependence
Remote sensing
Stacking
Support vector machines
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Summary:Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. Data fusion for remote sensing is becoming an important tool to improve classical approaches. In addition, artificial intelligence techniques such as machine learning or evolutive computation are often applied to improve the final LULC classification. In this paper, a hybrid artificial intelligence method based on an ensemble of multiple classifiers to improve LULC map accuracy is shown. The method works in two processing levels: first, an evolutionary algorithm (EA) for label-dependent feature weighting transforms the feature space by assigning different weights to every attribute depending on the class. Then a statistical raster from LIDAR and image data fusion is built following a pixel-oriented and feature-based strategy that uses a support vector machine (SVM) and a weighted k-NN restricted stacking. A classical SVM, the original restricted stacking (R-STACK) and the current improved method (EVOR-STACK) are compared. The results show that the evolutive approach obtains the best results in the context of the real data from a riparian area in southern Spain.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2011.02.020