A novel supervised feature extraction and classification fusion algorithm for land cover recognition of the off-land scenario

作者：Cui Yan^*; Jin Zhong; Jiang Jielin

来源：Neurocomputing, 2014, 140: 77-83.

DOI：10.1016/j.neucom.2014.03.034

• Feature extraction
• Sparse representation
• Dictionary learning
• Land cover recognition
• Classification

In this paper, a novel supervised feature extraction and classification fusion algorithm based on neighborhood preserving embedding (NPE) and sparse representation is proposed. Specifically, an optimal dictionary is adaptively learned to bate the trivial information of the original training data; then, in order to obtain the sparse representation coefficients, a sparse preserving embedding map is sought to reduce the dimensionality of high-dimensional data, and the test data is classified by the corresponding sparse representation coefficients. Finally, the novel supervised fusion algorithm is applied to the land cover recognition of the off-land scenario. Experimental results show that the proposed method leads to promising results in fusing feature extraction and classification.

• 出版日期2014-9-22
• 单位南京理工大学