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However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial\u2013spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial\u2013spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial\u2013spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial\u2013spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework.<\/jats:p>","DOI":"10.3390\/rs14153796","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Haojin","family":"Tang","sequence":"first","affiliation":[{"name":"ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Yanshan","family":"Li","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Zhiquan","family":"Huang","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Weixin","family":"Xie","sequence":"additional","affiliation":[{"name":"ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"ref_1","unstructured":"Chakraborty, T., and Trehan, U. 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