Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection
| Autor: | Li-Fang Hong, Fen Cai, Miao-Xia Guo, Ying-Yi Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
supervised sparse embedded preserving projection (SSEPP)
Computer science Quantitative Biology::Tissues and Organs 0211 other engineering and technologies Physics::Optics 02 engineering and technology lcsh:Technology lcsh:Chemistry 020204 information systems 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision Projection (set theory) sparse representation lcsh:QH301-705.5 Instrumentation 021101 geological & geomatics engineering dimensionality reduction Fluid Flow and Transfer Processes lcsh:T business.industry Process Chemistry and Technology General Engineering Hyperspectral imaging lcsh:QC1-999 Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION hyperspectral lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business lcsh:Physics |
| Zdroj: | Applied Sciences Volume 9 Issue 17 Applied Sciences, Vol 9, Iss 17, p 3583 (2019) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app9173583 |
| Popis: | Dimensionality reduction is an important research area for hyperspectral remote sensing images due to the redundancy of spectral information. Sparsity preserving projection (SPP) is a dimensionality reduction (DR) algorithm based on the l1-graph, which establishes the relations of samples by sparse representation. However, SPP is an unsupervised algorithm that ignores the label information of samples and the objective function of SPP instead, it only considers the reconstruction error, which means that the classification effect is constrained. In order to solve this problem, this paper proposes a dimensionality reduction algorithm called the supervised sparse embedded preserving projection (SSEPP) algorithm. SSEPP considers the manifold structure information of samples and makes full use of the label information available in order to enhance the discriminative ability of the projection subspace. While maintaining the sparse reconstruction error, the algorithm also minimizes the error between samples of the same class. Experiments were performed on an Indian Pines hyperspectral dataset and HJ1A-HSI remote sensing images from the Zhangjiang estuary in Southeastern China, respectively. The results show that the proposed method effectively improves its classification accuracy. |
| Databáze: | OpenAIRE |
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