Meta-Learner Hybrid Models to Classify Hyperspectral Images

Remote Sensing

卷:14

Abdelghani Dahou¹  Dalal AL-Alimi²  Yuxiang Shao²  Zhihua Cai²  Sakinatu Issaka³  Mohammed A. A. Al-qaness⁴ 

[1] Department of Mathematics and Computer Science, Faculty of Science and Technology, University of Ahmed DRAIA, Adrar 01000, Algeria;

[2] School of Computer Science, China University of Geosciences, Wuhan 430074, China;

[3] School of Environmental Studies, China University of Geosciences, Wuhan 430074, China;

[4] State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;

关键词: meta-learner;    hyperspectral image;    classification;    remote sensing images;    hybrid model;    feature fusion;   

DOI  :  10.3390/rs14041038

来源: DOAJ

【 摘 要 】

Hyperspectral (HS) images are adjacent band images that are generally used in remote-sensing applications. They have numerous spatial and spectral information bands that are extremely useful for material detection in various fields. However, their high dimensionality is a big challenge that affects their overall performance. A new data normalization method was developed to enhance the variations and data distribution using the output of principal component analysis (PCA) and quantile transformation, called QPCA. This paper also proposes a novel HS images classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernel of convolutional neural networks (CNN). The high-level model works to combine the output of the lower-level models and train them with the new input data, called meta-learner hybrid models (MLHM). The proposed MLHM framework with our external normalization (QPCA) improves the accuracy and outperforms other approaches using three well-known benchmark datasets. Moreover, the evaluation outcomes showed that the QPCA enhanced the framework accuracy by 13% for most models and datasets and others by more than 25%, and MLHM provided the best performance.

【 授权许可】

Unknown