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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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An artificial immune network approach to multi-sensor land use/land cover classification

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Title
An artificial immune network approach to multi-sensor land use/land cover classification
Author
Gong, BingleiIm, JunghoMountrakis, Giorgos
Keywords
Artificial immune networks; Artificial neural networks; Decision trees; Land use/land cover classification
Issue Date
2011-02
Publisher
ELSEVIER SCIENCE INC
Citation
REMOTE SENSING OF ENVIRONMENT, v.115, no.2, pp.600 - 614
Abstract
An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification. Major improvements of OPTINC compared to a typical immune network-based classification model (aiNet) include (1) preservation of the best antibodies of each land cover class from the antibody population suppression, which ensures that each land cover class is represented by at least one antibody; (2) mutation rates being self-adaptive according to the model performance between training generations, which improves the model convergence; and (3) incorporation of both Euclidean distance and spectral angle mapping distance to measure affinity between two feature vectors using a genetic algorithm-based optimization, which helps the model to better discriminate LULC classes with similar characteristics. OPTINC was evaluated using two sites with different remote sensing data: a residential area in Denver, CO with high-spatial resolution QuickBird image and LiDAR data, and a suburban area in Monticello, UT with HyMap hyperspectral imagery. A decision tree, a multilayer feed-forward back-propagation neural network, and aiNet were also tested for comparison. Classification accuracy, local homogeneity of classified images, and model sensitivity to training sample size were examined. OPTINC outperformed the other models with higher accuracy and more spatially cohesive land cover classes with limited salt-and-pepper noise. OPTINC was relatively less sensitive to training sample size than the neural network, followed by the decision tree.
URI
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DOI
10.1016/j.rse.2010.10.005
ISSN
0034-4257
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UEE_Journal Papers
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