Set-based similarity learning in subspace for agricultural remote sensing classification

文献类型： 外文期刊

第一作者： Tang, Yi

作者： Tang, Yi;Li, Xinrong

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关键词： Agricultural remote sensing;Point-based similarity;Set-based similarity;Similarity learning;Hyperspectral image classification

期刊名称：NEUROCOMPUTING （ 影响因子：5.719； 五年影响因子：4.986 ）

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收录情况： SCI

摘要： Similarity between spectral lines is key in the field of agricultural sensing classification, however, the measured spectral lines mostly mislead the classification because of unexpected disturbance in application. To enhance the accuracy of classification, similarity learning is introduced into agricultural remote sensing classification. Within the framework of similarity learning, the training set is generated by pairing the labeled spectral lines which means the size of training set for learning similarity is heavily increasing. Noticed this problem, a novel spectrum-set similarity learning algorithm is reported for balancing the gain in classification and the computational burden of learning similarity. Different from traditional point-based similarity, the spectrum-set similarity measures the similarity between two spectral sets which contain some spectral lines. Following the idea, set-based training set is generated by clustering the spectral lines in the point-based training set. Experimental results have shown the effectiveness and efficiency of learning spectrum-set similarity measure for agriculture sensing classification. (C) 2015 Elsevier B.V. All rights reserved.

分类号： TP3

  • 相关文献

[1]Two-dimensional nearest neighbor classification for agricultural remote sensing. Li, Xinrong,Tang, Yi. 2014

[2]Design and Implementation of Novel Agricultural Remote Sensing Image Classification Framework through Deep Neural Network and Multi-Feature Analysis. Zhang, Youzhi. 2015

[3]Bilateral filtering inspired locality preserving projections for hyperspectral images. Li, Xinrong,Pan, Jing,He, Yuqing,Liu, Changshu,Pan, Jing.

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