Abstract
A semi-supervised classification method for hyperspectral remote sensing images based on convolutional neural network (CNN) and modified tri-training is proposed. The abstract features are captured by training a CNN model with the pixels’ vectors as inputs. Based on the extracted high-level features, different classifiers will perform different outputs under the same training set, due to the different types of classifiers take on diverse characteristics. Thus, taking multiple classifiers’ results into consideration can integrate different prediction labels synthetically from a high level and can perform more credible results. At the meantime, the number of training samples of hyperspectral images is limited, which will hinder the classification effect. Illuminated by tri-training algorithm, we utilize triple different classifiers to classify the hyperspectral images based on the extracted high-level features in the semi-supervised mode. By utilizing triple classifiers jointly to train and update the training samples set when the number of labeled samples is limited. At the meantime, we pick the confident samples via randomize and majority vote into the training set for data editing during the iterative updating process. Experiments performed on two real hyperspectral images reveal that our method performs very well in terms of classification accuracy and effect.
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Acknowledgement
This work is supported in part by the National Science Foundation of China under Grant No. 61672276, the National Key Research and Development Program of China under Grant No. 2017YFB1400600, Jiangsu Natural Science Foundation of China under Grant No. BK20171037, the Program of Natural Science Research of Jiangsu colleges and universities under Grant No.17KJB170010, and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University.
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Zhang, G., Wang, J., Shi, G., Zhang, J., Dou, W. (2020). A Semi-supervised Classification Method for Hyperspectral Images by Triple Classifiers with Data Editing and Deep Learning. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_14
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DOI: https://doi.org/10.1007/978-3-030-48513-9_14
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