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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 431 - 443 (13page)
이용수
초록· 키워드
오류제보하기
Sparse unmixing has been proven to be an effective method for hyperspectral unmixing. Hyperspectral images contain rich spectral and spatial information. The means to make full use of spectral information, spatial information, and enhanced sparsity constraints are the main research directions to improve the accuracy of sparse unmixing. However, many algorithms only focus on one or two of these factors, because it is difficult to construct an unmixing model that considers all three factors. To address this issue, a novel algorithm called multiview-based spectral weighted and low-rank row-sparsity unmixing is proposed. A multiview data set is generated through spectral partitioning, and then spectral weighting is imposed on it to exploit the abundant spectral information. The row-sparsity approach, which controls the sparsity by the l<SUB>2,0</SUB> norm, outperforms the single-sparsity approach in many scenarios. Many algorithms use convex relaxation methods to solve the l<SUB>2,0</SUB> norm to avoid the NP-hard problem, but this will reduce sparsity and unmixing accuracy. In this paper, a row-hard-threshold function is introduced to solve the l<SUB>2,0</SUB> norm directly, which guarantees the sparsity of the results. The high spatial correlation of hyperspectral images is associated with low column rank; therefore, the low-rank constraint is adopted to utilize spatial information. Experiments with simulated and real data prove that the proposed algorithm can obtain better unmixing results.
목차
Ⅰ. INTRODUCTION
Ⅱ. SPARSE UNMIXING
Ⅲ. MULTIVIEW-BASED SPECTRAL WEIGHTED AND LOW-RANK ROW-SPARSITY UNMIXING
Ⅳ. EXPERIMENTAL RESULTS AND ANALYSIS
Ⅴ. CONCLUSION
REFERENCES
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