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

Abstract

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.