Journal
REMOTE SENSING
Volume 10, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs10030396
Keywords
deep belief networks; deep learning; texture feature enhancement; hyperspectral classification; band grouping
Categories
Funding
- National Nature Science Foundation of China [61571345, 91538101, 61501346, 61502367, 61701360]
- 111 project [B08038]
- Fundamental Research Funds for the Central Universities [JB170109]
- Natural Science Basic Research Plan in Shaanxi Province of China [2016JQ6023]
- China Postdoctoral Science Foundation [2017M623124]
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With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification process to enhance classification accuracy greatly. In this paper, a novel hyperspectral classification framework based on an optimal DBN and a novel texture feature enhancement (TFE) is proposed. Through band grouping, sample band selection and guided filtering, the texture features of hyperspectral data are improved. After TFE, the optimal DBN is employed on the hyperspectral reconstructed data for feature extraction and classification. Experimental results demonstrate that the proposed classification framework outperforms some state-of-the-art classification algorithms, and it can achieve outstanding hyperspectral classification performance. Furthermore, our proposed TFE method can play a significant role in improving classification accuracy.
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